Explained Honcho data model and inference needs

Summary

In Progress

Plan
Reviewed Honcho's ORM and agent pipeline.
Plan
Plan
Mapped database tables to inference invocation points.
Plan
Plan
Described core pairwise memory system and key models.
Plan

Explain Honcho's data model from the ground up. I notice that it makes inference requests for a number of different facets. What does it need inference for?

I’ll trace this from the actual ORM and agent pipeline rather than only the overview text, then map the tables to where inference is invoked.

>rg "class .*\(.*Base|class .*\(Base|__tablename__|Mapped\[" src/models.py src -g'*.py'

Chunk ID: b31992
Wall time: 0.0001 seconds
Process exited with code 0
Original token count: 5594
Output:
src/models.py:class Workspace(Base):
src/models.py:    __tablename__: str = "workspaces"
src/models.py:    id: Mapped[str] = mapped_column(TEXT, default=generate_nanoid, primary_key=[REDACTED]
src/models.py:    name: Mapped[str] = mapped_column(TEXT, unique=True)
src/models.py:    created_at: Mapped[datetime.datetime] = mapped_column(
src/models.py:    h_metadata: Mapped[dict[str, Any]] = mapped_column(
src/models.py:    internal_metadata: Mapped[dict[str, Any]] = mapped_column(
src/models.py:    configuration: Mapped[dict[str, Any]] = mapped_column(
src/models.py:class Peer(Base):
src/models.py:    __tablename__: str = "peers"
src/models.py:    id: Mapped[str] = mapped_column(TEXT, default=generate_nanoid, primary_key=[REDACTED]
src/models.py:    name: Mapped[str] = mapped_column(TEXT, nullable=False)
src/models.py:    h_metadata: Mapped[dict[str, Any]] = mapped_column(
src/models.py:    internal_metadata: Mapped[dict[str, Any]] = mapped_column(
src/models.py:    created_at: Mapped[datetime.datetime] = mapped_column(
src/models.py:    workspace_name: Mapped[str] = mapped_column(
src/models.py:    configuration: Mapped[dict[str, Any]] = mapped_column(
src/models.py:class Session(Base):
src/models.py:    __tablename__: str = "sessions"
src/models.py:    id: Mapped[str] = mapped_column(TEXT, primary_key=[REDACTED] default=generate_nanoid)
src/models.py:    name: Mapped[str] = mapped_column(TEXT)
src/models.py:    is_active: Mapped[bool] = mapped_column(default=True, server_default=text("true"))
src/models.py:    h_metadata: Mapped[dict[str, Any]] = mapped_column(
src/models.py:    internal_metadata: Mapped[dict[str, Any]] = mapped_column(
src/models.py:    created_at: Mapped[datetime.datetime] = mapped_column(
src/models.py:    workspace_name: Mapped[str] = mapped_column(
src/models.py:    configuration: Mapped[dict[str, Any]] = mapped_column(
src/models.py:class Message(Base):
src/models.py:    __tablename__: str = "messages"
src/models.py:    id: Mapped[int] = mapped_column(
src/models.py:    public_id: Mapped[str] = mapped_column(
src/models.py:    session_name: Mapped[str] = mapped_column(TEXT, nullable=False)
src/models.py:    content: Mapped[str] = mapped_column(TEXT)
src/models.py:    h_metadata: Mapped[dict[str, Any]] = mapped_column(
src/models.py:    internal_metadata: Mapped[dict[str, Any]] = mapped_column(
src/models.py:    token_count: Mapped[int] = mapped_column(Integer, default=0, nullable=False)
src/models.py:    seq_in_session: Mapped[int] = mapped_column(BigInteger, nullable=False)
src/models.py:    created_at: Mapped[datetime.datetime] = mapped_column(
src/models.py:    peer_name: Mapped[str] = mapped_column(TEXT, index=True)
src/models.py:    workspace_name: Mapped[str] = mapped_column(TEXT, index=True)
src/models.py:class MessageEmbedding(Base):
src/models.py:    __tablename__: str = "message_embeddings"
src/models.py:    id: Mapped[int] = mapped_column(
src/models.py:    content: Mapped[str] = mapped_column(TEXT)
src/models.py:    message_id: Mapped[str] = mapped_column(
src/models.py:    workspace_name: Mapped[str] = mapped_column(
src/models.py:    session_name: Mapped[str] = mapped_column(TEXT, nullable=False, index=True)
src/models.py:    peer_name: Mapped[str] = mapped_column(TEXT, nullable=False, index=True)
src/models.py:    created_at: Mapped[datetime.datetime] = mapped_column(
src/models.py:    sync_state: Mapped[VectorSyncState] = mapped_column(
src/models.py:    last_sync_at: Mapped[datetime.datetime | None] = mapped_column(
src/models.py:    sync_attempts: Mapped[int] = mapped_column(
src/models.py:class Collection(Base):
src/models.py:    __tablename__: str = "collections"
src/models.py:    id: Mapped[str] = mapped_column(TEXT, default=generate_nanoid, primary_key=[REDACTED]
src/models.py:    observer: Mapped[str] = mapped_column(TEXT, index=True)
src/models.py:    observed: Mapped[str] = mapped_column(TEXT, index=True)
src/models.py:    created_at: Mapped[datetime.datetime] = mapped_column(
src/models.py:    h_metadata: Mapped[dict[str, Any]] = mapped_column(
src/models.py:    internal_metadata: Mapped[dict[str, Any]] = mapped_column(
src/models.py:    workspace_name: Mapped[str] = mapped_column(
src/models.py:class Document(Base):
src/models.py:    __tablename__: str = "documents"
src/models.py:    id: Mapped[str] = mapped_column(TEXT, default=generate_nanoid, primary_key=[REDACTED]
src/models.py:    internal_metadata: Mapped[dict[str, Any]] = mapped_column(
src/models.py:    content: Mapped[str] = mapped_column(TEXT)
src/models.py:    level: Mapped[DocumentLevel] = mapped_column(
src/models.py:    times_derived: Mapped[int] = mapped_column(
src/models.py:    source_ids: Mapped[list[str] | None] = mapped_column(
src/models.py:    created_at: Mapped[datetime.datetime] = mapped_column(
src/models.py:    observer: Mapped[str] = mapped_column(TEXT, index=True)
src/models.py:    observed: Mapped[str] = mapped_column(TEXT, index=True)
src/models.py:    workspace_name: Mapped[str] = mapped_column(
src/models.py:    session_name: Mapped[str | None] = mapped_column(TEXT, nullable=True, index=True)
src/models.py:    deleted_at: Mapped[datetime.datetime | None] = mapped_column(
src/models.py:    sync_state: Mapped[VectorSyncState] = mapped_column(
src/models.py:    last_sync_at: Mapped[datetime.datetime | None] = mapped_column(
src/models.py:    sync_attempts: Mapped[int] = mapped_column(
src/models.py:class QueueItem(Base):
src/models.py:    __tablename__: str = "queue"
src/models.py:    id: Mapped[int] = mapped_column(
src/models.py:    session_id: Mapped[str | None] = mapped_column(
src/models.py:    work_unit_key: Mapped[str] = mapped_column(TEXT, nullable=False)
src/models.py:    task_type: Mapped[TaskType] = mapped_column(TEXT, nullable=False)
src/models.py:    payload: Mapped[dict[str, Any]] = mapped_column(JSONB, nullable=False)
src/models.py:    processed: Mapped[bool] = mapped_column(
src/models.py:    error: Mapped[str | None] = mapped_column(TEXT, nullable=True)
src/models.py:    created_at: Mapped[datetime.datetime] = mapped_column(
src/models.py:    workspace_name: Mapped[str | None] = mapped_column(
src/models.py:    message_id: Mapped[int | None] = mapped_column(
src/models.py:class ActiveQueueSession(Base):
src/models.py:    __tablename__: str = "active_queue_sessions"
src/models.py:    id: Mapped[str] = mapped_column(TEXT, default=generate_nanoid, primary_key=[REDACTED]
src/models.py:    work_unit_key: Mapped[str] = mapped_column(TEXT, unique=True)
src/models.py:    last_updated: Mapped[datetime.datetime] = mapped_column(
src/models.py:class WebhookEndpoint(Base):
src/models.py:    __tablename__: str = "webhook_endpoints"
src/models.py:    id: Mapped[str] = mapped_column(TEXT, default=generate_nanoid, primary_key=[REDACTED]
src/models.py:    workspace_name: Mapped[str] = mapped_column(
src/models.py:    url: Mapped[str] = mapped_column(TEXT, nullable=False)
src/models.py:    created_at: Mapped[datetime.datetime] = mapped_column(
src/models.py:class SessionPeer(Base):
src/models.py:    workspace_name: Mapped[str]
src/models.py:    session_name: Mapped[str]
src/models.py:    peer_name: Mapped[str]
src/models.py:    configuration: Mapped[dict[str, Any]]
src/models.py:    internal_metadata: Mapped[dict[str, Any]]
src/models.py:    joined_at: Mapped[datetime.datetime]
src/models.py:    left_at: Mapped[datetime.datetime | None]
src/webhooks/events.py:class BaseWebhookEvent(BaseModel):
src/webhooks/events.py:class QueueEmptyEvent(BaseWebhookEvent):
src/webhooks/events.py:class TestEvent(BaseWebhookEvent):
src/reconciler/scheduler.py:class ReconcilerTask(BaseModel):
src/utils/queue_payload.py:class BasePayload(BaseModel):
src/utils/queue_payload.py:class RepresentationPayload(BasePayload):
src/utils/queue_payload.py:class RepresentationPayloads(BasePayload):
src/utils/queue_payload.py:class SummaryPayload(BasePayload):
src/utils/queue_payload.py:class WebhookPayload(BasePayload):
src/utils/queue_payload.py:class DreamPayload(BasePayload):
src/utils/queue_payload.py:class DeletionPayload(BasePayload):
src/utils/queue_payload.py:class ReconcilerPayload(BasePayload):
src/vector_store/__init__.py:class VectorRecord(BaseModel):
src/vector_store/__init__.py:class VectorQueryResult(BaseModel):
src/dreamer/specialists.py:class DeductionSpecialist(BaseSpecialist):
src/dreamer/specialists.py:class InductionSpecialist(BaseSpecialist):
src/security.py:class JWTParams(BaseModel):
src/schemas/api.py:class WorkspaceBase(BaseModel):
src/schemas/api.py:class WorkspaceCreate(WorkspaceBase):
src/schemas/api.py:class WorkspaceGet(WorkspaceBase):
src/schemas/api.py:class WorkspaceUpdate(WorkspaceBase):
src/schemas/api.py:class Workspace(WorkspaceBase):
src/schemas/api.py:class PeerBase(BaseModel):
src/schemas/api.py:class PeerCreate(PeerBase):
src/schemas/api.py:class PeerGet(PeerBase):
src/schemas/api.py:class PeerUpdate(PeerBase):
src/schemas/api.py:class Peer(PeerBase):
src/schemas/api.py:class PeerRepresentationGet(BaseModel):
src/schemas/api.py:class RepresentationResponse(BaseModel):
src/schemas/api.py:class PeerCardResponse(BaseModel):
src/schemas/api.py:class PeerCardSet(BaseModel):
src/schemas/api.py:class MessageBase(BaseModel):
src/schemas/api.py:class MessageCreate(MessageBase):
src/schemas/api.py:class MessageGet(MessageBase):
src/schemas/api.py:class MessageUpdate(MessageBase):
src/schemas/api.py:class Message(MessageBase):
src/schemas/api.py:class MessageBatchCreate(BaseModel):
src/schemas/api.py:class MessageUploadCreate(BaseModel):
src/schemas/api.py:class SessionBase(BaseModel):
src/schemas/api.py:class SessionCreate(SessionBase):
src/schemas/api.py:class SessionGet(SessionBase):
src/schemas/api.py:class SessionUpdate(SessionBase):
src/schemas/api.py:class Session(SessionBase):
src/schemas/api.py:class Summary(BaseModel):
src/schemas/api.py:class SessionContext(SessionBase):
src/schemas/api.py:class PeerContext(BaseModel):
src/schemas/api.py:class SessionSummaries(SessionBase):
src/schemas/api.py:class ConclusionGet(BaseModel):
src/schemas/api.py:class Conclusion(BaseModel):
src/schemas/api.py:class ConclusionQuery(BaseModel):
src/schemas/api.py:class ConclusionCreate(BaseModel):
src/schemas/api.py:class ConclusionBatchCreate(BaseModel):
src/schemas/api.py:class MessageSearchOptions(BaseModel):
src/schemas/api.py:class DialecticOptions(BaseModel):
src/schemas/api.py:class DialecticResponse(BaseModel):
src/schemas/api.py:class DialecticStreamDelta(BaseModel):
src/schemas/api.py:class DialecticStreamChunk(BaseModel):
src/schemas/api.py:class SessionQueueStatus(BaseModel):
src/schemas/api.py:class QueueStatus(BaseModel):
src/schemas/api.py:class ScheduleDreamRequest(BaseModel):
src/schemas/api.py:class WebhookEndpointBase(BaseModel):
src/schemas/api.py:class WebhookEndpointCreate(WebhookEndpointBase):
src/schemas/api.py:class WebhookEndpoint(WebhookEndpointBase):
src/utils/representation.py:class ObservationMetadata(BaseModel):
src/utils/representation.py:class ExplicitObservationBase(BaseModel):
src/utils/representation.py:class DeductiveObservationBase(BaseModel):
src/utils/representation.py:class InductiveObservationBase(BaseModel):
src/utils/representation.py:class ContradictionObservationBase(BaseModel):
src/utils/representation.py:class PromptRepresentation(BaseModel):
src/utils/representation.py:class ExplicitObservation(ExplicitObservationBase, ObservationMetadata):
src/utils/representation.py:class DeductiveObservation(DeductiveObservationBase, ObservationMetadata):
src/utils/representation.py:class InductiveObservation(InductiveObservationBase, ObservationMetadata):
src/utils/representation.py:class ContradictionObservation(ContradictionObservationBase, ObservationMetadata):
src/utils/representation.py:class Representation(BaseModel):
src/models.py:class Workspace(Base):
src/models.py:    __tablename__: str = "workspaces"
src/models.py:    id: Mapped[str] = mapped_column(TEXT, default=generate_nanoid, primary_key=[REDACTED]
src/models.py:    name: Mapped[str] = mapped_column(TEXT, unique=True)
src/models.py:    created_at: Mapped[datetime.datetime] = mapped_column(
src/models.py:    h_metadata: Mapped[dict[str, Any]] = mapped_column(
src/models.py:    internal_metadata: Mapped[dict[str, Any]] = mapped_column(
src/models.py:    configuration: Mapped[dict[str, Any]] = mapped_column(
src/models.py:class Peer(Base):
src/models.py:    __tablename__: str = "peers"
src/models.py:    id: Mapped[str] = mapped_column(TEXT, default=generate_nanoid, primary_key=[REDACTED]
src/models.py:    name: Mapped[str] = mapped_column(TEXT, nullable=False)
src/models.py:    h_metadata: Mapped[dict[str, Any]] = mapped_column(
src/models.py:    internal_metadata: Mapped[dict[str, Any]] = mapped_column(
src/models.py:    created_at: Mapped[datetime.datetime] = mapped_column(
src/models.py:    workspace_name: Mapped[str] = mapped_column(
src/models.py:    configuration: Mapped[dict[str, Any]] = mapped_column(
src/models.py:class Session(Base):
src/models.py:    __tablename__: str = "sessions"
src/models.py:    id: Mapped[str] = mapped_column(TEXT, primary_key=[REDACTED] default=generate_nanoid)
src/models.py:    name: Mapped[str] = mapped_column(TEXT)
src/models.py:    is_active: Mapped[bool] = mapped_column(default=True, server_default=text("true"))
src/models.py:    h_metadata: Mapped[dict[str, Any]] = mapped_column(
src/models.py:    internal_metadata: Mapped[dict[str, Any]] = mapped_column(
src/models.py:    created_at: Mapped[datetime.datetime] = mapped_column(
src/models.py:    workspace_name: Mapped[str] = mapped_column(
src/models.py:    configuration: Mapped[dict[str, Any]] = mapped_column(
src/models.py:class Message(Base):
src/models.py:    __tablename__: str = "messages"
src/models.py:    id: Mapped[int] = mapped_column(
src/models.py:    public_id: Mapped[str] = mapped_column(
src/models.py:    session_name: Mapped[str] = mapped_column(TEXT, nullable=False)
src/models.py:    content: Mapped[str] = mapped_column(TEXT)
src/models.py:    h_metadata: Mapped[dict[str, Any]] = mapped_column(
src/models.py:    internal_metadata: Mapped[dict[str, Any]] = mapped_column(
src/models.py:    token_count: Mapped[int] = mapped_column(Integer, default=0, nullable=False)
src/models.py:    seq_in_session: Mapped[int] = mapped_column(BigInteger, nullable=False)
src/models.py:    created_at: Mapped[datetime.datetime] = mapped_column(
src/models.py:    peer_name: Mapped[str] = mapped_column(TEXT, index=True)
src/models.py:    workspace_name: Mapped[str] = mapped_column(TEXT, index=True)
src/models.py:class MessageEmbedding(Base):
src/models.py:    __tablename__: str = "message_embeddings"
src/models.py:    id: Mapped[int] = mapped_column(
src/models.py:    content: Mapped[str] = mapped_column(TEXT)
src/models.py:    message_id: Mapped[str] = mapped_column(
src/models.py:    workspace_name: Mapped[str] = mapped_column(
src/models.py:    session_name: Mapped[str] = mapped_column(TEXT, nullable=False, index=True)
src/models.py:    peer_name: Mapped[str] = mapped_column(TEXT, nullable=False, index=True)
src/models.py:    created_at: Mapped[datetime.datetime] = mapped_column(
src/models.py:    sync_state: Mapped[VectorSyncState] = mapped_column(
src/models.py:    last_sync_at: Mapped[datetime.datetime | None] = mapped_column(
src/models.py:    sync_attempts: Mapped[int] = mapped_column(
src/models.py:class Collection(Base):
src/models.py:    __tablename__: str = "collections"
src/models.py:    id: Mapped[str] = mapped_column(TEXT, default=generate_nanoid, primary_key=[REDACTED]
src/models.py:    observer: Mapped[str] = mapped_column(TEXT, index=True)
src/models.py:    observed: Mapped[str] = mapped_column(TEXT, index=True)
src/models.py:    created_at: Mapped[datetime.datetime] = mapped_column(
src/models.py:    h_metadata: Mapped[dict[str, Any]] = mapped_column(
src/models.py:    internal_metadata: Mapped[dict[str, Any]] = mapped_column(
src/models.py:    workspace_name: Mapped[str] = mapped_column(
src/models.py:class Document(Base):
src/models.py:    __tablename__: str = "documents"
src/models.py:    id: Mapped[str] = mapped_column(TEXT, default=generate_nanoid, primary_key=[REDACTED]
src/models.py:    internal_metadata: Mapped[dict[str, Any]] = mapped_column(
src/models.py:    content: Mapped[str] = mapped_column(TEXT)
src/models.py:    level: Mapped[DocumentLevel] = mapped_column(
src/models.py:    times_derived: Mapped[int] = mapped_column(
src/models.py:    source_ids: Mapped[list[str] | None] = mapped_column(
src/models.py:    created_at: Mapped[datetime.datetime] = mapped_column(
src/models.py:    observer: Mapped[str] = mapped_column(TEXT, index=True)
src/models.py:    observed: Mapped[str] = mapped_column(TEXT, index=True)
src/models.py:    workspace_name: Mapped[str] = mapped_column(
src/models.py:    session_name: Mapped[str | None] = mapped_column(TEXT, nullable=True, index=True)
src/models.py:    deleted_at: Mapped[datetime.datetime | None] = mapped_column(
src/models.py:    sync_state: Mapped[VectorSyncState] = mapped_column(
src/models.py:    last_sync_at: Mapped[datetime.datetime | None] = mapped_column(
src/models.py:    sync_attempts: Mapped[int] = mapped_column(
src/models.py:class QueueItem(Base):
src/models.py:    __tablename__: str = "queue"
src/models.py:    id: Mapped[int] = mapped_column(
src/models.py:    session_id: Mapped[str | None] = mapped_column(
src/models.py:    work_unit_key: Mapped[str] = mapped_column(TEXT, nullable=False)
src/models.py:    task_type: Mapped[TaskType] = mapped_column(TEXT, nullable=False)
src/models.py:    payload: Mapped[dict[str, Any]] = mapped_column(JSONB, nullable=False)
src/models.py:    processed: Mapped[bool] = mapped_column(
src/models.py:    error: Mapped[str | None] = mapped_column(TEXT, nullable=True)
src/models.py:    created_at: Mapped[datetime.datetime] = mapped_column(
src/models.py:    workspace_name: Mapped[str | None] = mapped_column(
src/models.py:    message_id: Mapped[int | None] = mapped_column(
src/models.py:class ActiveQueueSession(Base):
src/models.py:    __tablename__: str = "active_queue_sessions"
src/models.py:    id: Mapped[str] = mapped_column(TEXT, default=generate_nanoid, primary_key=[REDACTED]
src/models.py:    work_unit_key: Mapped[str] = mapped_column(TEXT, unique=True)
src/models.py:    last_updated: Mapped[datetime.datetime] = mapped_column(
src/models.py:class WebhookEndpoint(Base):
src/models.py:    __tablename__: str = "webhook_endpoints"
src/models.py:    id: Mapped[str] = mapped_column(TEXT, default=generate_nanoid, primary_key=[REDACTED]
src/models.py:    workspace_name: Mapped[str] = mapped_column(
src/models.py:    url: Mapped[str] = mapped_column(TEXT, nullable=False)
src/models.py:    created_at: Mapped[datetime.datetime] = mapped_column(
src/models.py:class SessionPeer(Base):
src/models.py:    workspace_name: Mapped[str]
src/models.py:    session_name: Mapped[str]
src/models.py:    peer_name: Mapped[str]
src/models.py:    configuration: Mapped[dict[str, Any]]
src/models.py:    internal_metadata: Mapped[dict[str, Any]]
src/models.py:    joined_at: Mapped[datetime.datetime]
src/models.py:    left_at: Mapped[datetime.datetime | None]
src/schemas/internal.py:class DocumentBase(BaseModel):
src/schemas/internal.py:class DocumentMetadata(BaseModel):
src/schemas/internal.py:class DocumentCreate(DocumentBase):
src/schemas/internal.py:class ObservationInput(BaseModel):
src/schemas/internal.py:class SessionCounts(BaseModel):
src/schemas/internal.py:class QueueCounts(BaseModel):
src/schemas/internal.py:class QueueStatusRow(BaseModel):
src/schemas/internal.py:class SessionPeerData(BaseModel):
src/schemas/internal.py:class MessageBulkData(BaseModel):
src/utils/work_unit.py:class ParsedWorkUnit(BaseModel):
src/schemas/configuration.py:class ReasoningConfiguration(BaseModel):
src/schemas/configuration.py:class PeerCardConfiguration(BaseModel):
src/schemas/configuration.py:class SummaryConfiguration(BaseModel):
src/schemas/configuration.py:class DreamConfiguration(BaseModel):
src/schemas/configuration.py:class WorkspaceConfiguration(BaseModel):
src/schemas/configuration.py:class MessageConfiguration(BaseModel):
src/schemas/configuration.py:class ResolvedReasoningConfiguration(BaseModel):
src/schemas/configuration.py:class ResolvedPeerCardConfiguration(BaseModel):
src/schemas/configuration.py:class ResolvedSummaryConfiguration(BaseModel):
src/schemas/configuration.py:class ResolvedDreamConfiguration(BaseModel):
src/schemas/configuration.py:class ResolvedConfiguration(BaseModel):
src/schemas/configuration.py:class PeerConfig(BaseModel):
src/telemetry/events/base.py:class BaseEvent(BaseModel):
src/telemetry/events/dream.py:class DreamRunEvent(BaseEvent):
src/telemetry/events/dream.py:class DreamSpecialistEvent(BaseEvent):
src/telemetry/events/representation.py:class RepresentationCompletedEvent(BaseEvent):
src/telemetry/events/agent.py:class AgentIterationEvent(BaseEvent):
src/telemetry/events/agent.py:class AgentToolConclusionsCreatedEvent(BaseEvent):
src/telemetry/events/agent.py:class AgentToolConclusionsDeletedEvent(BaseEvent):
src/telemetry/events/agent.py:class AgentToolPeerCardUpdatedEvent(BaseEvent):
src/telemetry/events/agent.py:class AgentToolSummaryCreatedEvent(BaseEvent):
src/telemetry/events/reconciliation.py:class SyncVectorsCompletedEvent(BaseEvent):
src/telemetry/events/reconciliation.py:class CleanupStaleItemsCompletedEvent(BaseEvent):
src/telemetry/events/dialectic.py:class DialecticCompletedEvent(BaseEvent):
src/telemetry/events/deletion.py:class DeletionCompletedEvent(BaseEvent):
src/config.py:class ModelOverrideSettings(BaseModel):
src/config.py:class PromptCachePolicy(BaseModel):
src/config.py:class FallbackModelSettings(BaseModel):
src/config.py:class ConfiguredModelSettings(BaseModel):
src/config.py:class ResolvedFallbackConfig(BaseModel):
src/config.py:class ModelConfig(BaseModel):
src/config.py:class ConfiguredEmbeddingModelSettings(BaseModel):
src/config.py:class EmbeddingModelConfig(BaseModel):
src/config.py:class TomlConfigSettingsSource(PydanticBaseSettingsSource):
src/config.py:class HonchoSettings(BaseSettings):
src/config.py:class DialecticLevelSettings(BaseModel):
src/config.py:class SurprisalSettings(BaseModel):
src/llm/types.py:class HonchoLLMCallResponse(BaseModel, Generic[T]):
src/llm/types.py:class HonchoLLMCallStreamChunk(BaseModel):
src/llm/caching.py:class GeminiCacheHandle(BaseModel):

>rg "inference|facet|representation|summary|dream|derive|observe|observations|peer_card|queue|honcho_llm_call|embedding" src tests docs -g'*.py' -g'*.md' -g'*.mdx'

Chunk ID: bbdcf3
Wall time: 0.0003 seconds
Process exited with code 0
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src/webhooks/README.md:* **Event Publishing (`webhooks/events.py`):** Defines the event types and allows us to publish new events to the processing queue.
src/webhooks/README.md:Note that the webhooks require the *deriver* process to be running to facilitate the delivery of the webhook.
docs/v3/guides/discord.mdx:assistant = honcho_client.peer(id="assistant", config={"observe_me": False})
src/webhooks/events.py:from src.utils.queue_payload import create_webhook_payload
src/webhooks/events.py:    QUEUE_EMPTY = "queue.empty"
src/webhooks/events.py:    """Webhook event for when a queue becomes empty."""
src/webhooks/events.py:    queue_type: str
src/webhooks/events.py:    observer: str | None = None
src/webhooks/events.py:    observed: str | None = None
src/webhooks/events.py:    Add a webhook event to our DB queue.
src/webhooks/events.py:        # It's stored directly on the queue item
src/webhooks/events.py:            queue_item = QueueItem(
src/webhooks/events.py:            db.add(queue_item)
tests/routes/test_peers.py:def test_get_peer_representation_with_session(
tests/routes/test_peers.py:    """Test peer representation with session_id parameter"""
tests/routes/test_peers.py:    # Test representation scoped to session
tests/routes/test_peers.py:        f"/v3/workspaces/{test_workspace.name}/peers/{test_peer.name}/representation",
tests/routes/test_peers.py:    assert "representation" in data
tests/routes/test_peers.py:    assert isinstance(data["representation"], str)
tests/routes/test_peers.py:def test_get_peer_representation_global(
tests/routes/test_peers.py:    """Test peer representation without session_id (global representation)"""
tests/routes/test_peers.py:    # Test global representation (no session_id)
tests/routes/test_peers.py:        f"/v3/workspaces/{test_workspace.name}/peers/{test_peer.name}/representation",
tests/routes/test_peers.py:    assert "representation" in data
tests/routes/test_peers.py:    assert isinstance(data["representation"], str)
tests/routes/test_peers.py:def test_get_peer_representation_with_target(
tests/routes/test_peers.py:    """Test peer representation with target parameter"""
tests/routes/test_peers.py:    # Test representation of target from observer's perspective
tests/routes/test_peers.py:        f"/v3/workspaces/{test_workspace.name}/peers/{test_peer.name}/representation",
tests/routes/test_peers.py:    assert "representation" in data
tests/routes/test_peers.py:    assert isinstance(data["representation"], str)
tests/routes/test_peers.py:def test_get_peer_representation_with_search_query(
tests/routes/test_peers.py:    """Test peer representation with search_query parameter"""
tests/routes/test_peers.py:    # Test representation with semantic search query
tests/routes/test_peers.py:        f"/v3/workspaces/{test_workspace.name}/peers/{test_peer.name}/representation",
tests/routes/test_peers.py:    assert "representation" in data
tests/routes/test_peers.py:def test_get_peer_representation_with_search_top_k(
tests/routes/test_peers.py:    """Test peer representation with search_top_k parameter"""
tests/routes/test_peers.py:            f"/v3/workspaces/{test_workspace.name}/peers/{test_peer.name}/representation",
tests/routes/test_peers.py:        assert "representation" in data
tests/routes/test_peers.py:def test_get_peer_representation_with_search_max_distance(
tests/routes/test_peers.py:    """Test peer representation with search_max_distance parameter"""
tests/routes/test_peers.py:            f"/v3/workspaces/{test_workspace.name}/peers/{test_peer.name}/representation",
tests/routes/test_peers.py:        assert "representation" in data
tests/routes/test_peers.py:def test_get_peer_representation_with_include_most_frequent(
tests/routes/test_peers.py:    """Test peer representation with include_most_frequent parameter"""
tests/routes/test_peers.py:        f"/v3/workspaces/{test_workspace.name}/peers/{test_peer.name}/representation",
tests/routes/test_peers.py:    assert "representation" in data
tests/routes/test_peers.py:        f"/v3/workspaces/{test_workspace.name}/peers/{test_peer.name}/representation",
tests/routes/test_peers.py:    assert "representation" in data
tests/routes/test_peers.py:def test_get_peer_representation_with_max_observations(
tests/routes/test_peers.py:    """Test peer representation with max_observations parameter"""
tests/routes/test_peers.py:    # Test with various max_observations values
tests/routes/test_peers.py:            f"/v3/workspaces/{test_workspace.name}/peers/{test_peer.name}/representation",
tests/routes/test_peers.py:                "max_observations": max_obs,
tests/routes/test_peers.py:        assert "representation" in data
tests/routes/test_peers.py:def test_get_peer_representation_with_all_parameters(
tests/routes/test_peers.py:    """Test peer representation with all optional parameters"""
tests/routes/test_peers.py:        f"/v3/workspaces/{test_workspace.name}/peers/{test_peer.name}/representation",
tests/routes/test_peers.py:            "max_observations": 30,
tests/routes/test_peers.py:    assert "representation" in data
tests/routes/test_peers.py:    assert isinstance(data["representation"], str)
tests/routes/test_peers.py:def test_get_peer_representation_boundary_values(
tests/routes/test_peers.py:    """Test peer representation with boundary values for numeric parameters"""
tests/routes/test_peers.py:        f"/v3/workspaces/{test_workspace.name}/peers/{test_peer.name}/representation",
tests/routes/test_peers.py:            "max_observations": 1,
tests/routes/test_peers.py:        f"/v3/workspaces/{test_workspace.name}/peers/{test_peer.name}/representation",
tests/routes/test_peers.py:            "max_observations": 100,
tests/routes/test_peers.py:def test_get_peer_representation_default_max_observations(
tests/routes/test_peers.py:    """Test that max_observations defaults to 25 when not provided"""
tests/routes/test_peers.py:    # Test without max_observations - should use default of 25
tests/routes/test_peers.py:        f"/v3/workspaces/{test_workspace.name}/peers/{test_peer.name}/representation",
tests/routes/test_peers.py:    assert "representation" in data
tests/routes/test_peers.py:def test_get_peer_card(client: TestClient, sample_data: tuple[Workspace, Peer]):
tests/routes/test_peers.py:    test_workspace, observer_peer = sample_data
tests/routes/test_peers.py:    # Create a second peer (the target/observed peer)
tests/routes/test_peers.py:    # Test getting observer's own card (should return null initially)
tests/routes/test_peers.py:        f"/v3/workspaces/{test_workspace.name}/peers/{observer_peer.name}/card"
tests/routes/test_peers.py:    assert data["peer_card"] is None
tests/routes/test_peers.py:        f"/v3/workspaces/{test_workspace.name}/peers/{observer_peer.name}/card",
tests/routes/test_peers.py:    assert data["peer_card"] is None
tests/routes/test_peers.py:async def test_get_peer_card_with_data(
tests/routes/test_peers.py:    test_workspace, observer_peer = sample_data
tests/routes/test_peers.py:    # Create a second peer (the target/observed peer)
tests/routes/test_peers.py:    # Set a self-card for the observer peer
tests/routes/test_peers.py:    await crud.set_peer_card(
tests/routes/test_peers.py:        observer=observer_peer.name,
tests/routes/test_peers.py:        observed=observer_peer.name,
tests/routes/test_peers.py:    # Set a card for the observer describing the target peer
tests/routes/test_peers.py:    await crud.set_peer_card(
tests/routes/test_peers.py:        observer=observer_peer.name,
tests/routes/test_peers.py:        observed=target_peer_name,
tests/routes/test_peers.py:    # Test getting observer's own card
tests/routes/test_peers.py:        f"/v3/workspaces/{test_workspace.name}/peers/{observer_peer.name}/card"
tests/routes/test_peers.py:    assert data["peer_card"] == self_card_content
tests/routes/test_peers.py:        f"/v3/workspaces/{test_workspace.name}/peers/{observer_peer.name}/card",
tests/routes/test_peers.py:    assert data["peer_card"] == target_card_content
tests/routes/test_peers.py:def test_set_peer_card(client: TestClient, sample_data: tuple[Workspace, Peer]):
tests/routes/test_peers.py:    test_workspace, observer_peer = sample_data
tests/routes/test_peers.py:    # Set the observer's own card
tests/routes/test_peers.py:        f"/v3/workspaces/{test_workspace.name}/peers/{observer_peer.name}/card",
tests/routes/test_peers.py:        json={"peer_card": self_card},
tests/routes/test_peers.py:    assert data["peer_card"] == self_card
tests/routes/test_peers.py:        f"/v3/workspaces/{test_workspace.name}/peers/{observer_peer.name}/card"
tests/routes/test_peers.py:    assert response.json()["peer_card"] == self_card
tests/routes/test_peers.py:        f"/v3/workspaces/{test_workspace.name}/peers/{observer_peer.name}/card",
tests/routes/test_peers.py:        json={"peer_card": target_card},
tests/routes/test_peers.py:    assert data["peer_card"] == target_card
tests/routes/test_peers.py:        f"/v3/workspaces/{test_workspace.name}/peers/{observer_peer.name}/card",
tests/routes/test_peers.py:    assert response.json()["peer_card"] == target_card
src/webhooks/webhook_delivery.py:from src.utils.queue_payload import WebhookPayload
docs/v3/guides/granola.mdx:4. Print a summary of what was transferred
docs/v3/guides/granola.mdx:| Meeting summary | Message from note creator | Multi-person calls store the summary |
docs/v3/guides/granola.mdx:Granola's transcript uses `Them:` for all non-creator speakers with no disambiguation — in a 4-person call, everyone else is just `Them:`. Rather than guess incorrectly, the script stores Granola's summary as your record of the meeting, with participants in metadata.
docs/v3/guides/granola.mdx:        f"{meeting_summary}",
docs/v3/guides/granola.mdx:            "mode": "summary",
docs/v3/guides/granola.mdx:The summary is attributed to you because it's *your* record of what happened. Granola captured your notes from a meeting where those people were present.
docs/v3/guides/granola.mdx:For each meeting, you choose the import mode: two-person (full attribution), summary, or skip. For multi-person calls that are actually 1:1s (extra participants listed but didn't speak), you can override the detection and select the actual speaker.
docs/v3/guides/granola.mdx:Because meetings live in a standard Honcho workspace, you can enrich peer representations with data from other channels:
docs/v3/guides/granola.mdx:| Missing transcripts | Free tier has no transcript access. The script falls back to summary content. |
docs/v3/guides/granola.mdx:        has_s = bool(extract_summary(m))
docs/v3/guides/granola.mdx:        label = "transcript+summary" if has_t and has_s else "transcript only" if has_t else "summary only" if has_s else "basic only"
docs/v3/guides/granola.mdx:def extract_summary(meeting: dict[str, Any]) -> str:
docs/v3/guides/granola.mdx:    """Extract best available summary text from meeting data."""
docs/v3/guides/granola.mdx:    for key in ("summary", "notes", "note", "meeting_notes", "description"):
docs/v3/guides/granola.mdx:        for tag in ("summary", "notes"):
docs/v3/guides/granola.mdx:def import_summary(
docs/v3/guides/granola.mdx:    """Import a meeting as a summary message."""
docs/v3/guides/granola.mdx:    summary = extract_summary(meeting)
docs/v3/guides/granola.mdx:    if not summary:
docs/v3/guides/granola.mdx:            summary = str(parsed.get("transcript", "")) if isinstance(parsed, dict) else raw_t
docs/v3/guides/granola.mdx:            summary = raw_t
docs/v3/guides/granola.mdx:    summary = summary or "No content available"
docs/v3/guides/granola.mdx:    messages = build_messages(me_peer, header + summary, metadata, created_at)
docs/v3/guides/granola.mdx:    print("  -> Imported as summary")
docs/v3/guides/granola.mdx:    - "summary": import as a single summary message
docs/v3/guides/granola.mdx:        print(f"  Content: {'summary available' if extract_summary(meeting) else 'metadata only'}")
docs/v3/guides/granola.mdx:            return ("summary", None)
docs/v3/guides/granola.mdx:        choice = input("  [Enter] summary / [2] 2-person / [k] skip: ").strip().lower()
docs/v3/guides/granola.mdx:            choice = input("  [Enter] summary / [2] 2-person / [k] skip: ").strip().lower()
docs/v3/guides/granola.mdx:                return ("summary", None)
docs/v3/guides/granola.mdx:        return ("summary", None)
docs/v3/guides/granola.mdx:    choice = input("  [Enter] summary / [k] skip: ").strip().lower()
docs/v3/guides/granola.mdx:        choice = input("  [Enter] summary / [k] skip: ").strip().lower()
docs/v3/guides/granola.mdx:    return ("summary", None)
docs/v3/guides/granola.mdx:                        import_summary(honcho, session, me_peer_id, m, metadata, created_at)
docs/snippets/cli-commands.mdx:<ParamField path="--observer" type="string">
docs/snippets/cli-commands.mdx:<ParamField path="--observed" type="string">
docs/snippets/cli-commands.mdx:<ParamField path="--observer" type="string">
docs/snippets/cli-commands.mdx:<ParamField path="--observed" type="string">
docs/snippets/cli-commands.mdx:<ParamField path="--observer" type="string">
docs/snippets/cli-commands.mdx:<ParamField path="--observed" type="string">
docs/snippets/cli-commands.mdx:<ParamField path="--observer" type="string">
docs/snippets/cli-commands.mdx:<ParamField path="--observed" type="string">
docs/snippets/cli-commands.mdx:Verify config and connectivity. Scope with -w / -p to check workspace, peer, and queue health.
docs/snippets/cli-commands.mdx:List, create, chat with, search, and manage peers and their representations.
docs/snippets/cli-commands.mdx:<ParamField path="--observe-me" type="boolean">
docs/snippets/cli-commands.mdx:  Whether Honcho will form a representation of this peer. Negate with `--no-observe-me`.
docs/snippets/cli-commands.mdx:<Accordion title="representation">
docs/snippets/cli-commands.mdx:Get the formatted representation for a peer.
docs/snippets/cli-commands.mdx:honcho peer representation [<peer_id>]
docs/snippets/cli-commands.mdx:  Target peer to get representation about.
docs/snippets/cli-commands.mdx:<ParamField path="--summary" type="boolean" default="true">
docs/snippets/cli-commands.mdx:  Include summary. Negate with `--no-summary`.
docs/snippets/cli-commands.mdx:<Accordion title="representation">
docs/snippets/cli-commands.mdx:Get the representation of a peer within a session.
docs/snippets/cli-commands.mdx:honcho session representation <peer_id> [<session_id>]
docs/snippets/cli-commands.mdx:<Accordion title="queue-status">
docs/snippets/cli-commands.mdx:Get queue processing status.
docs/snippets/cli-commands.mdx:honcho workspace queue-status
docs/snippets/cli-commands.mdx:<ParamField path="--observer" type="string">
docs/snippets/cli-commands.mdx:  Filter by observer peer.
src/startup/__init__.py:"""Startup-time validators that gate API/deriver boot."""
src/startup/__init__.py:from src.startup.embedding_validator import (
src/startup/__init__.py:    validate_embedding_schema,
src/startup/__init__.py:__all__ = ("StartupValidationError", "validate_embedding_schema")
tests/routes/test_files.py:    configuration = {"skip_deriver": True, "custom_flag": "test"}
tests/routes/test_files.py:    configuration = {"skip_deriver": False, "test_mode": True}
src/startup/embedding_validator.py:"""Startup validator for the embedding pipeline.
src/startup/embedding_validator.py:ones. Full enumeration is available via `uv run python scripts/configure_embeddings.py --report`.
src/startup/embedding_validator.py:_EMBEDDING_TABLES: tuple[str, ...] = ("documents", "message_embeddings")
src/startup/embedding_validator.py:    """Raised when the embedding configuration cannot be reconciled with the
src/startup/embedding_validator.py:    queue task is processed.
src/startup/embedding_validator.py:async def validate_embedding_schema(
src/startup/embedding_validator.py:    """Validate that the embedding schema matches the configured dimension.
src/startup/embedding_validator.py:    Run after the DB pool is initialized and before the embedding client is
src/startup/embedding_validator.py:    Returns a mapping of table name -> raw ``atttypmod`` for the embedding
src/startup/embedding_validator.py:            f"could not validate embedding schema: {underlying}"
src/startup/embedding_validator.py:    raise StartupValidationError("embedding schema introspection did not run")
src/startup/embedding_validator.py:          AND a.attname = 'embedding'
src/startup/embedding_validator.py:        listing = ", ".join(sorted(f"{schema}.{t}.embedding" for t in missing))
src/startup/embedding_validator.py:                f"{schema}.{table}.embedding has no declared vector dimension"
src/startup/embedding_validator.py:                + " `uv run python scripts/configure_embeddings.py`."
src/startup/embedding_validator.py:                f"{schema}.{table}.embedding dim ({actual}) does not match"
src/startup/embedding_validator.py:                + " `uv run python scripts/configure_embeddings.py`"
src/startup/embedding_validator.py:    - Document namespaces — one per existing ``(workspace, observer, observed)``
src/startup/embedding_validator.py:    ``configure_embeddings --report`` for full enumeration when a hard
src/startup/embedding_validator.py:    for workspace_name, observer, observed in collection_keys:
src/startup/embedding_validator.py:                observer=observer,
src/startup/embedding_validator.py:                observed=observed,
src/startup/embedding_validator.py:            + " `uv run python scripts/configure_embeddings.py --report`."
src/startup/embedding_validator.py:    """Pull up to ``limit`` ``(workspace_name, observer, observed)`` triples,
src/startup/embedding_validator.py:        select(Collection.workspace_name, Collection.observer, Collection.observed)
docs/v3/guides/migrations/mem0.mdx:**Superior Performance** - Higher accuracy on memory retrieval benchmarks with faster inference times (more details soon!).
docs/v3/guides/migrations/mem0.mdx:**Advanced Multi-Peer Sessions** - Honcho offers configurable observation settings (who builds memories about whom), representation-based queries between participants, and first-class peer objects.
docs/v3/guides/migrations/mem0.mdx:For the best results, we recommend importing your raw messages directly into Honcho. This gives Honcho the full context to build rich, accurate representations and enables features like session summaries.
docs/v3/guides/migrations/mem0.mdx:That's it! The user's Mem0 memories are now searchable in Honcho as conclusions. For richer representations with deductive reasoning and session summaries, consider importing your raw messages as described in the [Step-by-Step Migration](#step-by-step-migration) section.
docs/v3/guides/migrations/mem0.mdx:Importing raw user messages gives Honcho the full conversational context to build the most accurate representations. We recommend using a data structure that preserves the session and peer structure.
docs/v3/guides/migrations/mem0.mdx:  Learn more about inference-powered queries
docs/v3/guides/migrations/mem0.mdx:| `session.representation(peer)` | Cached psychological analysis (mental state, intentions) | Real-time adaptation |
docs/v3/guides/integrations/mcp.mdx:**Peers** — `create_peer`, `list_peers`, `chat`, `get_peer_card`, `set_peer_card`, `get_peer_context`, `get_representation`
docs/v3/guides/integrations/mcp.mdx:**System** — `schedule_dream`, `get_queue_status`
docs/v3/guides/integrations/mcp.mdx:On the first conversation there won't be much — but after a few exchanges, Honcho's background reasoning will start building a representation of you. Ask again after a couple of conversations and you'll see the difference.
docs/v3/documentation/reference/cli.mdx:    honcho conclusion list --observer <peer_id> --json
docs/v3/documentation/reference/cli.mdx:    honcho conclusion search "topic" --…190138 tokens truncated…s/deriver/test_deriver_processing.py:            legacy_observer = legacy_payload.get("observer")
tests/deriver/test_deriver_processing.py:            observers = [legacy_observer] if legacy_observer else []
tests/deriver/test_deriver_processing.py:        assert observers == ["peer_observer"]
tests/deriver/test_deriver_processing.py:    def test_new_payload_observers_list_used_directly(self):
tests/deriver/test_deriver_processing.py:        """Test that new payloads with 'observers' list are used directly."""
tests/deriver/test_deriver_processing.py:            "observers": ["peer1", "peer2"],
tests/deriver/test_deriver_processing.py:            "observed": "peer3",
tests/deriver/test_deriver_processing.py:            "task_type": "representation",
tests/deriver/test_deriver_processing.py:        observers = new_payload.get("observers")
tests/deriver/test_deriver_processing.py:        if observers is None:
tests/deriver/test_deriver_processing.py:            legacy_observer = new_payload.get("observer")
tests/deriver/test_deriver_processing.py:            observers = [legacy_observer] if legacy_observer else []
tests/deriver/test_deriver_processing.py:        assert observers == ["peer1", "peer2"]
tests/deriver/test_deriver_processing.py:    def test_empty_payload_results_in_empty_observers_list(self):
tests/deriver/test_deriver_processing.py:        """Test that payloads with neither observer nor observers return empty list."""
tests/deriver/test_deriver_processing.py:            "observed": "peer_observed",
tests/deriver/test_deriver_processing.py:            "task_type": "representation",
tests/deriver/test_deriver_processing.py:        observers = empty_payload.get("observers")
tests/deriver/test_deriver_processing.py:        if observers is None:
tests/deriver/test_deriver_processing.py:            legacy_observer = empty_payload.get("observer")
tests/deriver/test_deriver_processing.py:            observers = [legacy_observer] if legacy_observer else []
tests/deriver/test_deriver_processing.py:        assert observers == []
tests/deriver/test_deriver_processing.py:    # async def test_representation_batch_uses_earliest_cutoff(
tests/deriver/test_deriver_processing.py:    #         "src.deriver.deriver.summarizer.get_session_context_formatted",
tests/deriver/test_deriver_processing.py:    #     # Provide a stub working representation so embedding lookups are skipped.
tests/deriver/test_deriver_processing.py:    #         "src.crud.get_working_representation",
tests/deriver/test_deriver_processing.py:    #         "src.deriver.deriver.CertaintyReasoner.reason",
tests/deriver/test_deriver_processing.py:    #     await process_representation_tasks_batch(
tests/deriver/test_deriver_processing.py:    #         observer=alice.name,
tests/deriver/test_deriver_processing.py:    #         observed=alice.name,
tests/crud/test_representation_manager.py:from src.crud.representation import RepresentationManager
tests/crud/test_representation_manager.py:from src.utils.representation import (
tests/crud/test_representation_manager.py:def _saved_observations(mock_save: AsyncMock):
tests/crud/test_representation_manager.py:    if "all_observations" in call.kwargs:
tests/crud/test_representation_manager.py:        return call.kwargs["all_observations"]
tests/crud/test_representation_manager.py:    raise AssertionError("missing all_observations in await args")
tests/crud/test_representation_manager.py:            observer=test_peer.name,
tests/crud/test_representation_manager.py:            observed=test_peer2.name,
tests/crud/test_representation_manager.py:            observer=test_peer.name,
tests/crud/test_representation_manager.py:            observed=test_peer2.name,
tests/crud/test_representation_manager.py:            observer=test_peer.name,
tests/crud/test_representation_manager.py:            observed=test_peer2.name,
tests/crud/test_representation_manager.py:            observer=test_peer.name,
tests/crud/test_representation_manager.py:            observed=test_peer2.name,
tests/crud/test_representation_manager.py:    async def test_query_documents_most_derived_excludes_soft_deleted(
tests/crud/test_representation_manager.py:        """Soft-deleted documents must not appear in the most-derived query."""
tests/crud/test_representation_manager.py:        # Create two documents with different times_derived
tests/crud/test_representation_manager.py:            observer=test_peer.name,
tests/crud/test_representation_manager.py:            observed=test_peer2.name,
tests/crud/test_representation_manager.py:            times_derived=5,
tests/crud/test_representation_manager.py:            observer=test_peer.name,
tests/crud/test_representation_manager.py:            observed=test_peer2.name,
tests/crud/test_representation_manager.py:            content="Deleted high-derived observation",
tests/crud/test_representation_manager.py:            times_derived=100,
tests/crud/test_representation_manager.py:        # Soft-delete the high-derived one
tests/crud/test_representation_manager.py:        results = await manager._query_documents_most_derived(db_session, top_k=10)  # pyright: ignore[reportPrivateUsage]
tests/crud/test_representation_manager.py:    async def test_save_representation_filters_blank_observations_before_embedding(
tests/crud/test_representation_manager.py:            observer="observer",
tests/crud/test_representation_manager.py:            observed="observed",
tests/crud/test_representation_manager.py:        representation = Representation(
tests/crud/test_representation_manager.py:            patch("src.crud.representation.tracked_db", _fake_tracked_db),
tests/crud/test_representation_manager.py:                "src.crud.representation.embedding_client.simple_batch_embed",
tests/crud/test_representation_manager.py:                "_save_representation_internal",
tests/crud/test_representation_manager.py:            saved = await manager.save_representation(
tests/crud/test_representation_manager.py:                representation,
tests/crud/test_representation_manager.py:                    dream=SimpleNamespace(enabled=False)
tests/crud/test_representation_manager.py:        saved_observations = _saved_observations(mock_save)
tests/crud/test_representation_manager.py:        assert len(saved_observations) == 1
tests/crud/test_representation_manager.py:        assert saved_observations[0].content == "useful observation"
tests/crud/test_representation_manager.py:    async def test_save_representation_filters_blank_deductive_observations(self):
tests/crud/test_representation_manager.py:            observer="observer",
tests/crud/test_representation_manager.py:            observed="observed",
tests/crud/test_representation_manager.py:        representation = Representation(
tests/crud/test_representation_manager.py:            patch("src.crud.representation.tracked_db", _fake_tracked_db),
tests/crud/test_representation_manager.py:                "src.crud.representation.embedding_client.simple_batch_embed",
tests/crud/test_representation_manager.py:                "_save_representation_internal",
tests/crud/test_representation_manager.py:            saved = await manager.save_representation(
tests/crud/test_representation_manager.py:                representation,
tests/crud/test_representation_manager.py:                    dream=SimpleNamespace(enabled=False)
tests/crud/test_representation_manager.py:        saved_observations = _saved_observations(mock_save)
tests/crud/test_representation_manager.py:        assert len(saved_observations) == 1
tests/crud/test_representation_manager.py:        assert isinstance(saved_observations[0], DeductiveObservation)
tests/crud/test_representation_manager.py:        assert saved_observations[0].conclusion == "inferred conclusion"
tests/crud/test_representation_manager.py:    async def test_save_representation_skips_all_blank_observations(self):
tests/crud/test_representation_manager.py:            observer="observer",
tests/crud/test_representation_manager.py:            observed="observed",
tests/crud/test_representation_manager.py:        representation = Representation(
tests/crud/test_representation_manager.py:            patch("src.crud.representation.tracked_db", _fake_tracked_db),
tests/crud/test_representation_manager.py:                "src.crud.representation.embedding_client.simple_batch_embed",
tests/crud/test_representation_manager.py:                "_save_representation_internal",
tests/crud/test_representation_manager.py:            saved = await manager.save_representation(
tests/crud/test_representation_manager.py:                representation,
tests/crud/test_representation_manager.py:                    dream=SimpleNamespace(enabled=False)
tests/crud/test_peer_card.py:from src.crud.peer_card import construct_peer_card_label, get_peer_card, set_peer_card
tests/crud/test_peer_card.py:async def test_peer_card_get_set_roundtrip(
tests/crud/test_peer_card.py:        await get_peer_card(
tests/crud/test_peer_card.py:            db_session, workspace.name, observer=peer.name, observed=peer.name
tests/crud/test_peer_card.py:    await set_peer_card(
tests/crud/test_peer_card.py:        observer=peer.name,
tests/crud/test_peer_card.py:        observed=peer.name,
tests/crud/test_peer_card.py:        await get_peer_card(
tests/crud/test_peer_card.py:            db_session, workspace.name, observer=peer.name, observed=peer.name
tests/crud/test_peer_card.py:    await set_peer_card(
tests/crud/test_peer_card.py:        db_session, workspace.name, value_2, observer=peer.name, observed=peer.name
tests/crud/test_peer_card.py:        await get_peer_card(
tests/crud/test_peer_card.py:            db_session, workspace.name, observer=peer.name, observed=peer.name
tests/crud/test_peer_card.py:async def test_get_peer_card_missing_peer_raises(
tests/crud/test_peer_card.py:        await get_peer_card(
tests/crud/test_peer_card.py:            observer="missing-peer",
tests/crud/test_peer_card.py:            observed="missing-peer",
tests/crud/test_peer_card.py:async def test_get_peer_card_missing_workspace_raises(
tests/crud/test_peer_card.py:        await get_peer_card(
tests/crud/test_peer_card.py:            observer=peer.name,
tests/crud/test_peer_card.py:            observed=peer.name,
tests/crud/test_peer_card.py:async def test_peer_card_empty_list(
tests/crud/test_peer_card.py:    await set_peer_card(
tests/crud/test_peer_card.py:        observer=peer.name,
tests/crud/test_peer_card.py:        observed=peer.name,
tests/crud/test_peer_card.py:        await get_peer_card(
tests/crud/test_peer_card.py:            db_session, workspace.name, observer=peer.name, observed=peer.name
tests/crud/test_peer_card.py:async def test_peer_card_multiple_lines(
tests/crud/test_peer_card.py:    await set_peer_card(
tests/crud/test_peer_card.py:        observer=peer.name,
tests/crud/test_peer_card.py:        observed=peer.name,
tests/crud/test_peer_card.py:    result = await get_peer_card(
tests/crud/test_peer_card.py:        db_session, workspace.name, observer=peer.name, observed=peer.name
tests/crud/test_peer_card.py:async def test_peer_card_different_observer_observed(
tests/crud/test_peer_card.py:    """Test peer card with different observer and observed peers."""
tests/crud/test_peer_card.py:    # Peer1 observes peer2
tests/crud/test_peer_card.py:    await set_peer_card(
tests/crud/test_peer_card.py:        observer=peer1.name,
tests/crud/test_peer_card.py:        observed=peer2.name,
tests/crud/test_peer_card.py:    # Peer2 observes peer1
tests/crud/test_peer_card.py:    await set_peer_card(
tests/crud/test_peer_card.py:        observer=peer2.name,
tests/crud/test_peer_card.py:        observed=peer1.name,
tests/crud/test_peer_card.py:        await get_peer_card(
tests/crud/test_peer_card.py:            db_session, workspace.name, observer=peer1.name, observed=peer2.name
tests/crud/test_peer_card.py:        await get_peer_card(
tests/crud/test_peer_card.py:            db_session, workspace.name, observer=peer2.name, observed=peer1.name
tests/crud/test_peer_card.py:async def test_peer_card_self_and_other_observations(
tests/crud/test_peer_card.py:    """Test that a peer can have both a self-observation and observations of others."""
tests/crud/test_peer_card.py:    await set_peer_card(
tests/crud/test_peer_card.py:        observer=peer1.name,
tests/crud/test_peer_card.py:        observed=peer1.name,
tests/crud/test_peer_card.py:    await set_peer_card(
tests/crud/test_peer_card.py:        observer=peer1.name,
tests/crud/test_peer_card.py:        observed=peer2.name,
tests/crud/test_peer_card.py:        await get_peer_card(
tests/crud/test_peer_card.py:            db_session, workspace.name, observer=peer1.name, observed=peer1.name
tests/crud/test_peer_card.py:        await get_peer_card(
tests/crud/test_peer_card.py:            db_session, workspace.name, observer=peer1.name, observed=peer2.name
tests/crud/test_peer_card.py:async def test_peer_card_multiple_observers_same_observed(
tests/crud/test_peer_card.py:    """Test that multiple peers can observe the same peer with different cards."""
tests/crud/test_peer_card.py:    # Both peer2 and peer3 observe peer1
tests/crud/test_peer_card.py:    await set_peer_card(
tests/crud/test_peer_card.py:        observer=peer2.name,
tests/crud/test_peer_card.py:        observed=peer1.name,
tests/crud/test_peer_card.py:    await set_peer_card(
tests/crud/test_peer_card.py:        observer=peer3.name,
tests/crud/test_peer_card.py:        observed=peer1.name,
tests/crud/test_peer_card.py:    # Each observer should have their own independent card
tests/crud/test_peer_card.py:        await get_peer_card(
tests/crud/test_peer_card.py:            db_session, workspace.name, observer=peer2.name, observed=peer1.name
tests/crud/test_peer_card.py:        await get_peer_card(
tests/crud/test_peer_card.py:            db_session, workspace.name, observer=peer3.name, observed=peer1.name
tests/crud/test_peer_card.py:async def test_peer_card_update_does_not_affect_others(
tests/crud/test_peer_card.py:    await set_peer_card(
tests/crud/test_peer_card.py:        observer=peer1.name,
tests/crud/test_peer_card.py:        observed=peer1.name,
tests/crud/test_peer_card.py:    await set_peer_card(
tests/crud/test_peer_card.py:        observer=peer1.name,
tests/crud/test_peer_card.py:        observed=peer2.name,
tests/crud/test_peer_card.py:    await set_peer_card(
tests/crud/test_peer_card.py:        observer=peer1.name,
tests/crud/test_peer_card.py:        observed=peer1.name,
tests/crud/test_peer_card.py:        await get_peer_card(
tests/crud/test_peer_card.py:            db_session, workspace.name, observer=peer1.name, observed=peer1.name
tests/crud/test_peer_card.py:        await get_peer_card(
tests/crud/test_peer_card.py:            db_session, workspace.name, observer=peer1.name, observed=peer2.name
tests/crud/test_peer_card.py:async def test_peer_card_special_characters(
tests/crud/test_peer_card.py:    await set_peer_card(
tests/crud/test_peer_card.py:        observer=peer.name,
tests/crud/test_peer_card.py:        observed=peer.name,
tests/crud/test_peer_card.py:    result = await get_peer_card(
tests/crud/test_peer_card.py:        db_session, workspace.name, observer=peer.name, observed=peer.name
tests/crud/test_peer_card.py:async def test_peer_card_large_content(
tests/crud/test_peer_card.py:    # Create a large peer card with many observations
tests/crud/test_peer_card.py:    await set_peer_card(
tests/crud/test_peer_card.py:        observer=peer.name,
tests/crud/test_peer_card.py:        observed=peer.name,
tests/crud/test_peer_card.py:    result = await get_peer_card(
tests/crud/test_peer_card.py:        db_session, workspace.name, observer=peer.name, observed=peer.name
tests/crud/test_peer_card.py:    assert construct_peer_card_label(observer="a", observed="a") == "peer_card"
tests/crud/test_peer_card.py:    assert construct_peer_card_label(observer="a", observed="b") == "b_peer_card"
tests/crud/test_peer_card.py:def test_construct_peer_card_label_with_special_chars():
tests/crud/test_peer_card.py:        construct_peer_card_label(observer="peer-1", observed="peer-2")
tests/crud/test_peer_card.py:        == "peer-2_peer_card"
tests/crud/test_peer_card.py:        construct_peer_card_label(observer="peer_1", observed="peer_2")
tests/crud/test_peer_card.py:        == "peer_2_peer_card"
tests/crud/test_peer_card.py:    # Test with same observer and observed with special chars
tests/crud/test_peer_card.py:        construct_peer_card_label(observer="peer-1", observed="peer-1") == "peer_card"
tests/crud/test_peer_card.py:def test_construct_peer_card_label_edge_cases():
tests/crud/test_peer_card.py:    assert construct_peer_card_label(observer="", observed="") == "peer_card"
tests/crud/test_peer_card.py:    assert construct_peer_card_label(observer="a", observed="") == "_peer_card"
tests/crud/test_peer_card.py:        construct_peer_card_label(observer=long_name, observed=long_name) == "peer_card"
tests/crud/test_peer_card.py:        construct_peer_card_label(observer="a", observed=long_name)
tests/crud/test_peer_card.py:        == f"{long_name}_peer_card"
tests/crud/test_workspace.py:            observer=test_peer.name,
tests/crud/test_workspace.py:            observed=test_peer.name,
tests/crud/test_workspace.py:            observer=test_peer.name,
tests/crud/test_workspace.py:            observed=test_peer.name,
tests/crud/test_workspace.py:            observer=test_peer.name,
tests/crud/test_workspace.py:            observed=test_peer.name,
tests/crud/test_workspace.py:    async def test_delete_workspace_cascade_queue_items(
tests/crud/test_workspace.py:        """Test that deleting a workspace cascades to delete queue items"""
tests/crud/test_workspace.py:        # Create queue item with work_unit_key containing workspace name
tests/crud/test_workspace.py:        queue_item = models.QueueItem(
tests/crud/test_workspace.py:            work_unit_key=[REDACTED]
tests/crud/test_workspace.py:            task_type="representation",
tests/crud/test_workspace.py:        db_session.add(queue_item)
tests/crud/test_workspace.py:        # Verify queue item exists
tests/crud/test_workspace.py:        queue_items = result.scalars().all()
tests/crud/test_workspace.py:        assert len(queue_items) == 1
tests/crud/test_workspace.py:        # Verify queue item is deleted
tests/crud/test_workspace.py:        queue_items = result.scalars().all()
tests/crud/test_workspace.py:        assert len(queue_items) == 0
tests/crud/test_workspace.py:    async def test_delete_workspace_cascade_active_queue_sessions(
tests/crud/test_workspace.py:        """Test that deleting a workspace cascades to delete active queue sessions"""
tests/crud/test_workspace.py:        # Create active queue session with work_unit_key containing workspace name
tests/crud/test_workspace.py:        active_queue = models.ActiveQueueSession(
tests/crud/test_workspace.py:            work_unit_key=[REDACTED]
tests/crud/test_workspace.py:        db_session.add(active_queue)
tests/crud/test_workspace.py:        # Verify active queue session exists
tests/crud/test_workspace.py:        active_queues = result.scalars().all()
tests/crud/test_workspace.py:        assert len(active_queues) == 1
tests/crud/test_workspace.py:        # Verify active queue session is deleted
tests/crud/test_workspace.py:        active_queues = result.scalars().all()
tests/crud/test_workspace.py:        assert len(active_queues) == 0
tests/crud/test_workspace.py:            observer=test_peer.name,
tests/crud/test_workspace.py:            observed=peer2.name,
tests/crud/test_workspace.py:            observer=test_peer.name,
tests/crud/test_workspace.py:            observed=peer2.name,

>ls src && rg --files src | sed -n '1,160p'

Chunk ID: bcbd31
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 786
Output:
__init__.py
__pycache__
cache
config.py
crud
db.py
dependencies.py
deriver
dialectic
dreamer
embedding_client.py
exceptions.py
llm
main.py
models.py
reconciler
routers
schemas
security.py
startup
telemetry
utils
vector_store
webhooks
src/dependencies.py
src/webhooks/README.md
src/webhooks/events.py
src/webhooks/webhook_delivery.py
src/startup/__init__.py
src/startup/embedding_validator.py
src/main.py
src/reconciler/scheduler.py
src/reconciler/sync_vectors.py
src/reconciler/__init__.py
src/reconciler/queue_cleanup.py
src/telemetry/emitter.py
src/telemetry/events/base.py
src/telemetry/events/representation.py
src/telemetry/events/dialectic.py
src/telemetry/events/deletion.py
src/telemetry/events/agent.py
src/telemetry/events/dream.py
src/telemetry/events/__init__.py
src/telemetry/events/reconciliation.py
src/telemetry/sentry.py
src/telemetry/prometheus/__init__.py
src/telemetry/prometheus/metrics.py
src/telemetry/metrics_collector.py
src/telemetry/__init__.py
src/telemetry/reasoning_traces.py
src/telemetry/logging.py
src/exceptions.py
src/deriver/queue_manager.py
src/deriver/__main__.py
src/deriver/deriver.py
src/deriver/consumer.py
src/deriver/prompts.py
src/deriver/__init__.py
src/deriver/enqueue.py
src/crud/representation.py
src/crud/workspace.py
src/crud/collection.py
src/crud/deriver.py
src/crud/document.py
src/crud/peer.py
src/crud/message.py
src/crud/__init__.py
src/crud/session.py
src/crud/webhook.py
src/crud/peer_card.py
src/dreamer/dream_scheduler.py
src/dreamer/orchestrator.py
src/dreamer/__init__.py
src/dreamer/specialists.py
src/dreamer/trees/base.py
src/dreamer/trees/prototype.py
src/dreamer/trees/lsh.py
src/dreamer/trees/rptree.py
src/dreamer/trees/__init__.py
src/dreamer/trees/graph.py
src/dreamer/trees/covertree.py
src/dreamer/trees/sklearn_wrapper.py
src/dreamer/surprisal.py
src/vector_store/lancedb.py
src/vector_store/__init__.py
src/vector_store/turbopuffer.py
src/schemas/api.py
src/schemas/__init__.py
src/schemas/internal.py
src/schemas/configuration.py
src/utils/agent_tools.py
src/utils/json_parser.py
src/utils/representation.py
src/utils/filter.py
src/utils/summarizer.py
src/utils/search.py
src/utils/queue_payload.py
src/utils/formatting.py
src/utils/types.py
src/utils/tokens.py
src/utils/__init__.py
src/utils/work_unit.py
src/utils/config_helpers.py
src/utils/files.py
src/__init__.py
src/security.py
src/models.py
src/cache/__init__.py
src/cache/client.py
src/embedding_client.py
src/config.py
src/llm/executor.py
src/llm/tool_loop.py
src/llm/caching.py
src/llm/api.py
src/llm/runtime.py
src/llm/types.py
src/llm/__init__.py
src/llm/request_builder.py
src/llm/backends/anthropic.py
src/llm/backends/openai.py
src/llm/backends/gemini.py
src/llm/backends/__init__.py
src/llm/conversation.py
src/llm/registry.py
src/llm/credentials.py
src/llm/history_adapters.py
src/llm/backend.py
src/llm/structured_output.py
src/dialectic/chat.py
src/dialectic/prompts.py
src/dialectic/core.py
src/dialectic/__init__.py
src/db.py
src/routers/workspaces.py
src/routers/webhooks.py
src/routers/messages.py
src/routers/conclusions.py
src/routers/keys.py
src/routers/peers.py
src/routers/sessions.py

The schema is centered on peer-to-peer observations, not just users and chats. I’m narrowing in on the persisted models and the code paths that turn raw messages into conclusions, cards, summaries, and dialectic responses.

>sed -n '1,280p' src/models.py

Chunk ID: 7cdc6d
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2421
Output:
import datetime
from logging import getLogger
from typing import Any, final

from dotenv import load_dotenv
from nanoid import generate as generate_nanoid
from pgvector.sqlalchemy import Vector
from sqlalchemy import (
    BigInteger,
    Boolean,
    CheckConstraint,
    Column,
    DateTime,
    ForeignKey,
    ForeignKeyConstraint,
    Identity,
    Index,
    Integer,
    Table,
    UniqueConstraint,
    text,
)
from sqlalchemy.dialects.postgresql import JSONB, TEXT
from sqlalchemy.orm import Mapped, MappedColumn, mapped_column, relationship
from sqlalchemy.sql import func
from typing_extensions import override

from src.config import settings
from src.utils.types import DocumentLevel, TaskType, VectorSyncState

from .db import Base

load_dotenv(override=True)

_VECTOR_DIM: int = settings.EMBEDDING.VECTOR_DIMENSIONS

logger = getLogger(__name__)


# Association table for many-to-many relationship between sessions and peers
session_peers_table = Table(
    "session_peers",
    Base.metadata,
    Column(
        "workspace_name",
        TEXT,
        ForeignKey("workspaces.name"),
        primary_key=[REDACTED]
        nullable=False,
    ),
    Column(
        "session_name",
        TEXT,
        primary_key=[REDACTED]
        nullable=False,
    ),
    Column("peer_name", TEXT, primary_key=[REDACTED] nullable=False),
    Column(
        "configuration",
        JSONB,
        default=dict,
        nullable=False,
        server_default=text("'{}'::jsonb"),
    ),
    Column(
        "internal_metadata",
        JSONB,
        default=dict,
        nullable=False,
        server_default=text("'{}'::jsonb"),
    ),
    Column(
        "joined_at",
        DateTime(timezone=True),
        nullable=False,
        server_default=func.now(),
    ),
    Column(
        "left_at",
        DateTime(timezone=True),
        nullable=True,
    ),
    # Composite foreign key constraint for sessions
    ForeignKeyConstraint(
        ["session_name", "workspace_name"],
        ["sessions.name", "sessions.workspace_name"],
    ),
    # Composite foreign key constraint for peers
    ForeignKeyConstraint(
        ["peer_name", "workspace_name"],
        ["peers.name", "peers.workspace_name"],
    ),
)


@final
class Workspace(Base):
    __tablename__: str = "workspaces"
    id: Mapped[str] = mapped_column(TEXT, default=generate_nanoid, primary_key=[REDACTED]
    name: Mapped[str] = mapped_column(TEXT, unique=True)
    created_at: Mapped[datetime.datetime] = mapped_column(
        DateTime(timezone=True), server_default=func.now(), index=True
    )
    h_metadata: Mapped[dict[str, Any]] = mapped_column(
        "metadata", JSONB, default=dict, server_default=text("'{}'::jsonb")
    )
    internal_metadata: Mapped[dict[str, Any]] = mapped_column(
        "internal_metadata", JSONB, default=dict, server_default=text("'{}'::jsonb")
    )
    configuration: Mapped[dict[str, Any]] = mapped_column(
        JSONB, default=dict, server_default=text("'{}'::jsonb")
    )

    sessions = relationship(
        "Session", back_populates="workspace", cascade="all, delete, delete-orphan"
    )
    peers = relationship(
        "Peer", back_populates="workspace", cascade="all, delete, delete-orphan"
    )
    webhook_endpoints = relationship("WebhookEndpoint", back_populates="workspace")

    __table_args__ = (
        CheckConstraint("length(id) = 21", name="id_length"),
        CheckConstraint("length(name) <= 512", name="name_length"),
        CheckConstraint("id ~ '^[A-Za-z0-9_-]+$'", name="id_format"),
    )


@final
class Peer(Base):
    __tablename__: str = "peers"
    id: Mapped[str] = mapped_column(TEXT, default=generate_nanoid, primary_key=[REDACTED]
    name: Mapped[str] = mapped_column(TEXT, nullable=False)
    h_metadata: Mapped[dict[str, Any]] = mapped_column(
        "metadata", JSONB, default=dict, server_default=text("'{}'::jsonb")
    )
    internal_metadata: Mapped[dict[str, Any]] = mapped_column(
        "internal_metadata", JSONB, default=dict, server_default=text("'{}'::jsonb")
    )
    created_at: Mapped[datetime.datetime] = mapped_column(
        DateTime(timezone=True), server_default=func.now(), index=True
    )
    workspace_name: Mapped[str] = mapped_column(
        ForeignKey("workspaces.name"), nullable=False, index=True
    )
    configuration: Mapped[dict[str, Any]] = mapped_column(
        JSONB, default=dict, server_default=text("'{}'::jsonb")
    )

    workspace = relationship("Workspace", back_populates="peers")
    sessions = relationship(
        "Session", secondary=session_peers_table, back_populates="peers"
    )

    __table_args__ = (
        UniqueConstraint("name", "workspace_name"),
        CheckConstraint("length(id) = 21", name="id_length"),
        CheckConstraint("length(name) <= 512", name="name_length"),
        CheckConstraint("id ~ '^[A-Za-z0-9_-]+$'", name="id_format"),
    )

    def __repr__(self) -> str:
        return f"Peer(id={self.id}, name={self.name}, workspace_name={self.workspace_name}, created_at={self.created_at}, h_metadata={self.h_metadata}, configuration={self.configuration})"


@final
class Session(Base):
    __tablename__: str = "sessions"
    id: Mapped[str] = mapped_column(TEXT, primary_key=[REDACTED] default=generate_nanoid)
    name: Mapped[str] = mapped_column(TEXT)
    is_active: Mapped[bool] = mapped_column(default=True, server_default=text("true"))
    h_metadata: Mapped[dict[str, Any]] = mapped_column(
        "metadata", JSONB, default=dict, server_default=text("'{}'::jsonb")
    )
    internal_metadata: Mapped[dict[str, Any]] = mapped_column(
        "internal_metadata", JSONB, default=dict, server_default=text("'{}'::jsonb")
    )
    created_at: Mapped[datetime.datetime] = mapped_column(
        DateTime(timezone=True), server_default=func.now(), index=True
    )
    workspace_name: Mapped[str] = mapped_column(
        ForeignKey("workspaces.name"), nullable=False, index=True
    )
    configuration: Mapped[dict[str, Any]] = mapped_column(
        JSONB, default=dict, server_default=text("'{}'::jsonb")
    )

    workspace = relationship("Workspace", back_populates="sessions")
    peers = relationship(
        "Peer", secondary=session_peers_table, back_populates="sessions"
    )
    messages = relationship("Message", back_populates="session")

    __table_args__ = (
        UniqueConstraint("name", "workspace_name"),
        CheckConstraint("length(name) <= 512", name="name_length"),
        CheckConstraint("length(id) = 21", name="id_length"),
        CheckConstraint("id ~ '^[A-Za-z0-9_-]+$'", name="id_format"),
    )

    def __repr__(self) -> str:
        return f"Session(id={self.id}, name={self.name}, workspace_name={self.workspace_name}, is_active={self.is_active}, created_at={self.created_at}, h_metadata={self.h_metadata})"


@final
class Message(Base):
    __tablename__: str = "messages"
    id: Mapped[int] = mapped_column(
        BigInteger, Identity(), primary_key=[REDACTED] autoincrement=True
    )
    public_id: Mapped[str] = mapped_column(
        TEXT,
        unique=True,
        default=generate_nanoid,
    )
    # NOTE: Messages in Honcho 2.0 could historically be stored outside of a session.
    # We have since assigned all of these messages to a default session.
    session_name: Mapped[str] = mapped_column(TEXT, nullable=False)
    content: Mapped[str] = mapped_column(TEXT)
    h_metadata: Mapped[dict[str, Any]] = mapped_column(
        "metadata", JSONB, default=dict, server_default=text("'{}'::jsonb")
    )
    internal_metadata: Mapped[dict[str, Any]] = mapped_column(
        "internal_metadata", JSONB, default=dict, server_default=text("'{}'::jsonb")
    )
    token_count: Mapped[int] = mapped_column(Integer, default=0, nullable=False)
    seq_in_session: Mapped[int] = mapped_column(BigInteger, nullable=False)

    created_at: Mapped[datetime.datetime] = mapped_column(
        DateTime(timezone=True), server_default=func.now(), index=True
    )
    # Note: Foreign key relationships established via composite ForeignKeyConstraint below
    peer_name: Mapped[str] = mapped_column(TEXT, index=True)
    workspace_name: Mapped[str] = mapped_column(TEXT, index=True)

    session = relationship("Session", back_populates="messages")

    __table_args__ = (
        CheckConstraint("length(public_id) = 21", name="public_id_length"),
        CheckConstraint("public_id ~ '^[A-Za-z0-9_-]+$'", name="public_id_format"),
        CheckConstraint("length(content) <= 65535", name="content_length"),
        # Composite foreign key constraint for sessions
        ForeignKeyConstraint(
            ["session_name", "workspace_name"],
            ["sessions.name", "sessions.workspace_name"],
        ),
        # Composite foreign key constraint for peers
        ForeignKeyConstraint(
            ["peer_name", "workspace_name"],
            ["peers.name", "peers.workspace_name"],
        ),
        Index(
            "ix_messages_session_lookup",
            "session_name",
            "id",
            postgresql_include=["id", "created_at"],
        ),
        UniqueConstraint(
            "workspace_name",
            "session_name",
            "seq_in_session",
        ),
        # Full text search index on content column
        Index(
            "ix_messages_content_gin",
            text("to_tsvector('english', content)"),
            postgresql_using="gin",
        ),
    )

    @override
    def __repr__(self) -> str:
        return f"Message(id={self.id}, session_name={self.session_name}, peer_name={self.peer_name}, content={self.content})"


@final
class MessageEmbedding(Base):
    __tablename__: str = "message_embeddings"

    id: Mapped[int] = mapped_column(

>sed -n '1,260p' src/utils/representation.py

Chunk ID: 7366d7
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2511
Output:
from collections.abc import Sequence
from datetime import datetime
from typing import Any

from pydantic import BaseModel, Field, field_validator

from src import models
from src.utils.formatting import parse_datetime_iso


def _strip_microseconds_and_timezone(timestamp: datetime) -> datetime:
    """
    Remove microseconds and timezone info from a datetime for stable string formatting.
    """
    return timestamp.replace(microsecond=0, tzinfo=None)


def flatten_message_ids(
    message_ids: list[int] | list[list[int]] | list[tuple[int, int]],
) -> list[int]:
    """
    Flatten message_ids that may be in old tuple format or nested list format.

    This handles backwards compatibility with the old schema where message_ids
    was list[tuple[int, int]] representing ranges, and the new schema where
    it's list[int] representing individual message IDs.

    Args:
        message_ids: Either a flat list of ints, nested list, or list of tuples

    Returns:
        A flat list of unique message IDs, sorted

    Examples:
        [1, 2, 3] -> [1, 2, 3]
        [[1, 2], [3, 4]] -> [1, 2, 3, 4]
        [(105, 105)] -> [105]
        [[105, 105]] -> [105]
    """
    result: list[int] = []
    for item in message_ids:
        if isinstance(item, (list | tuple)):
            # Nested list or tuple - flatten it
            result.extend(item)
        else:
            # Already flat
            result.append(item)
    # Remove duplicates and sort
    return sorted(set(result))


class ObservationMetadata(BaseModel):
    id: str = Field(default="", description="Document ID for this observation")
    created_at: datetime
    message_ids: list[int]
    session_name: str | None = None


class ExplicitObservationBase(BaseModel):
    content: str = Field(description="The explicit observation")


class DeductiveObservationBase(BaseModel):
    source_ids: list[str] = Field(
        description="Document IDs of premise observations for tree traversal",
        default_factory=list,
    )
    premises: list[str] = Field(
        description="Human-readable premise text for display",
        default_factory=list,
    )
    conclusion: str = Field(description="The deductive conclusion")


class InductiveObservationBase(BaseModel):
    """Base model for inductive observations - patterns, generalizations, and personality insights."""

    source_ids: list[str] = Field(
        description="Document IDs of source observations for tree traversal",
        default_factory=list,
    )
    sources: list[str] = Field(
        description="Human-readable source text for display",
        default_factory=list,
    )
    pattern_type: str = Field(
        description="Type of pattern: 'preference', 'behavior', 'personality', 'tendency', 'correlation'",
        default="pattern",
    )
    conclusion: str = Field(description="The inductive generalization or pattern")
    confidence: str = Field(
        description="Confidence level: 'high', 'medium', 'low'",
        default="medium",
    )


class ContradictionObservationBase(BaseModel):
    """Base model for contradiction observations - when user has made conflicting statements."""

    source_ids: list[str] = Field(
        description="Document IDs of the contradicting observations",
        default_factory=list,
    )
    sources: list[str] = Field(
        description="Human-readable text of the contradicting statements",
        default_factory=list,
    )
    content: str = Field(description="Description of the contradiction")


class PromptRepresentation(BaseModel):
    """
    The representation format that is used when getting structured output from an LLM.
    """

    explicit: list[ExplicitObservationBase] = Field(
        description="Facts LITERALLY stated by the user - direct quotes or clear paraphrases only, no interpretation or inference. Example: ['The user is 25 years old', 'The user has a dog named Rover']",
        default_factory=list,
    )

    @field_validator("explicit", mode="before")
    @classmethod
    def convert_none_to_empty_list(cls, v: Any) -> Any:
        """Convert None to empty list - handles LLMs returning null instead of []."""
        if v is None:
            return []
        return v


class ExplicitObservation(ExplicitObservationBase, ObservationMetadata):
    """Explicit observation with content and metadata."""

    def __str__(self) -> str:
        return f"[{_strip_microseconds_and_timezone(self.created_at)}] {self.content}"

    def str_with_id(self) -> str:
        """Format with ID prefix for use by agents that need to reference observations."""
        id_prefix = f"[id:{self.id}] " if self.id else ""
        return f"{id_prefix}[{_strip_microseconds_and_timezone(self.created_at)}] {self.content}"

    def __hash__(self) -> int:
        """
        Make ExplicitObservation hashable for use in sets.
        """
        return hash((self.content, self.created_at, self.session_name))

    def __eq__(self, other: object) -> bool:
        """
        Define equality for ExplicitObservation objects.
        Two observations are equal if all their fields match.
        """
        if not isinstance(other, ExplicitObservation):
            return False
        return (
            self.content == other.content
            and self.created_at == other.created_at
            and self.session_name == other.session_name
        )


class DeductiveObservation(DeductiveObservationBase, ObservationMetadata):
    """Deductive observation with multiple premises and one conclusion, plus metadata."""

    def __str__(self) -> str:
        premises_text = "\n".join(f"    - {premise}" for premise in self.premises)
        return f"[{_strip_microseconds_and_timezone(self.created_at)}] {self.conclusion}\n{premises_text}"

    def str_with_id(self) -> str:
        """Format with ID prefix for use by agents that need to reference observations."""
        id_prefix = f"[id:{self.id}] " if self.id else ""
        premises_text = "\n".join(f"    - {premise}" for premise in self.premises)
        return f"{id_prefix}[{_strip_microseconds_and_timezone(self.created_at)}] {self.conclusion}\n{premises_text}"

    def str_no_timestamps(self) -> str:
        premises_text = "\n".join(f"    - {premise}" for premise in self.premises)
        return f"{self.conclusion}\n{premises_text}"

    def __hash__(self) -> int:
        """
        Make DeductiveObservation hashable for use in sets. NOTE: premises are not included in the hash.
        """
        return hash((self.conclusion, self.created_at, self.session_name))

    def __eq__(self, other: object) -> bool:
        """
        Define equality for DeductiveObservation objects.
        Two observations are equal if all their fields match -- NOTE: premises are not included in the equality check.
        """
        if not isinstance(other, DeductiveObservation):
            return False
        return (
            self.conclusion == other.conclusion
            and self.created_at == other.created_at
            and self.session_name == other.session_name
        )


class InductiveObservation(InductiveObservationBase, ObservationMetadata):
    """Inductive observation with sources, pattern type, and confidence, plus metadata."""

    def __str__(self) -> str:
        sources_text = ""
        if self.sources:
            source_lines = [f"    - {source}" for source in self.sources]
            sources_text = "\n" + "\n".join(source_lines)
        return f"[{_strip_microseconds_and_timezone(self.created_at)}] [{self.confidence}] {self.conclusion}{sources_text}"

    def str_with_id(self) -> str:
        """Format with ID prefix for use by agents that need to reference observations."""
        id_prefix = f"[id:{self.id}] " if self.id else ""
        sources_text = ""
        if self.sources:
            source_lines = [f"    - {source}" for source in self.sources]
            sources_text = "\n" + "\n".join(source_lines)
        return f"{id_prefix}[{_strip_microseconds_and_timezone(self.created_at)}] [{self.confidence}] {self.conclusion}{sources_text}"

    def str_no_timestamps(self) -> str:
        sources_text = ""
        if self.sources:
            source_lines = [f"    - {source}" for source in self.sources]
            sources_text = "\n" + "\n".join(source_lines)
        return f"[{self.confidence}] {self.conclusion}{sources_text}"

    def __hash__(self) -> int:
        """Make InductiveObservation hashable for use in sets."""
        return hash((self.conclusion, self.created_at, self.session_name))

    def __eq__(self, other: object) -> bool:
        """Define equality for InductiveObservation objects."""
        if not isinstance(other, InductiveObservation):
            return False
        return (
            self.conclusion == other.conclusion
            and self.created_at == other.created_at
            and self.session_name == other.session_name
        )


class ContradictionObservation(ContradictionObservationBase, ObservationMetadata):
    """Contradiction observation - notes when user has made conflicting statements, plus metadata."""

    def __str__(self) -> str:
        sources_text = ""
        if self.sources:
            source_lines = [f"    - {source}" for source in self.sources]
            sources_text = "\n" + "\n".join(source_lines)
        return f"[{_strip_microseconds_and_timezone(self.created_at)}] CONTRADICTION: {self.content}{sources_text}"

    def str_with_id(self) -> str:
        """Format with ID prefix for use by agents that need to reference observations."""
        id_prefix = f"[id:{self.id}] " if self.id else ""
        sources_text = ""
        if self.sources:
            source_lines = [f"    - {source}" for source in self.sources]
            sources_text = "\n" + "\n".join(source_lines)
        return f"{id_prefix}[{_strip_microseconds_and_timezone(self.created_at)}] CONTRADICTION: {self.content}{sources_text}"

    def str_no_timestamps(self) -> str:
        sources_text = ""
        if self.sources:

>sed -n '1,280p' src/schemas/configuration.py

Chunk ID: 6262a5
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1826
Output:
"""Configuration schemas for hierarchical settings resolution.

Covers workspace, session, and message-level configuration as well as
the fully-resolved variants used at runtime.
"""

from enum import Enum
from typing import Any, Self, cast

from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator

from src.config import settings
from src.utils.tokens import estimate_tokens


class DreamType(str, Enum):
    """Types of dreams that can be triggered."""

    OMNI = "omni"


class ReasoningConfiguration(BaseModel):
    enabled: bool | None = Field(
        default=None,
        description="Whether to enable reasoning functionality.",
    )
    custom_instructions: str | None = Field(
        default=None,
        description="Optional custom instructions for the reasoning system on this workspace/session/message. Rejected if they exceed the deriver custom-instruction token cap.",
    )

    @field_validator("custom_instructions")
    @classmethod
    def validate_custom_instructions(cls, value: str | None) -> str | None:
        return _validate_custom_instructions_budget(value)


class PeerCardConfiguration(BaseModel):
    use: bool | None = Field(
        default=None,
        description="Whether to use peer card related to this peer during reasoning process.",
    )
    create: bool | None = Field(
        default=None,
        description="Whether to generate peer card based on content.",
    )


class SummaryConfiguration(BaseModel):
    enabled: bool | None = Field(
        default=None,
        description="Whether to enable summary functionality.",
    )
    messages_per_short_summary: int | None = Field(
        default=None,
        ge=10,
        description="Number of messages per short summary. Must be positive, greater than or equal to 10, and less than messages_per_long_summary.",
    )
    messages_per_long_summary: int | None = Field(
        default=None,
        ge=20,
        description="Number of messages per long summary. Must be positive, greater than or equal to 20, and greater than messages_per_short_summary.",
    )

    @model_validator(mode="after")
    def validate_summary_thresholds(self) -> Self:
        """Validate that short summary threshold <= long summary threshold."""
        short = self.messages_per_short_summary
        long = self.messages_per_long_summary

        if short is not None and long is not None and short >= long:
            raise ValueError(
                "messages_per_short_summary must be less than messages_per_long_summary"
            )

        return self


class DreamConfiguration(BaseModel):
    enabled: bool | None = Field(
        default=None,
        description="Whether to enable dream functionality. If reasoning is disabled, dreams will also be disabled and this setting will be ignored.",
    )


def _validate_custom_instructions_budget(
    custom_instructions: str | None,
) -> str | None:
    if custom_instructions is None:
        return None
    if not custom_instructions.strip():
        return custom_instructions

    max_tokens = settings.DERIVER.MAX_CUSTOM_INSTRUCTIONS_TOKENS
    if max_tokens <= 0:
        raise ValueError("custom_instructions are not enabled for this deployment")

    if estimate_tokens(custom_instructions) > max_tokens:
        raise ValueError(
            f"custom_instructions exceeds DERIVER.MAX_CUSTOM_INSTRUCTIONS_TOKENS ({max_tokens} tokens)"
        )

    return custom_instructions


class WorkspaceConfiguration(BaseModel):
    """
    The set of options that can be in a workspace DB-level configuration dictionary.

    All fields are optional. Session-level configuration overrides workspace-level configuration, which overrides global configuration.
    """

    model_config = ConfigDict(extra="allow")  # pyright: ignore

    reasoning: ReasoningConfiguration | None = Field(
        default=None,
        description="Configuration for reasoning functionality.",
    )
    peer_card: PeerCardConfiguration | None = Field(
        default=None,
        description="Configuration for peer card functionality. If reasoning is disabled, peer cards will also be disabled and these settings will be ignored.",
    )
    summary: SummaryConfiguration | None = Field(
        default=None,
        description="Configuration for summary functionality.",
    )
    dream: DreamConfiguration | None = Field(
        default=None,
        description="Configuration for dream functionality. If reasoning is disabled, dreams will also be disabled and these settings will be ignored.",
    )


class SessionConfiguration(WorkspaceConfiguration):
    """
    The set of options that can be in a session DB-level configuration dictionary.

    All fields are optional. Session-level configuration overrides workspace-level configuration, which overrides global configuration.
    """

    pass


class MessageConfiguration(BaseModel):
    """
    The set of options that can be in a message DB-level configuration dictionary.

    All fields are optional. Message-level configuration overrides all other configurations.
    """

    reasoning: ReasoningConfiguration | None = Field(
        default=None,
        description="Configuration for reasoning functionality.",
    )


class ResolvedReasoningConfiguration(BaseModel):
    enabled: bool
    custom_instructions: str | None = None

    @field_validator("custom_instructions")
    @classmethod
    def validate_custom_instructions(cls, value: str | None) -> str | None:
        return _validate_custom_instructions_budget(value)


class ResolvedPeerCardConfiguration(BaseModel):
    use: bool
    create: bool


class ResolvedSummaryConfiguration(BaseModel):
    enabled: bool
    messages_per_short_summary: int
    messages_per_long_summary: int


class ResolvedDreamConfiguration(BaseModel):
    enabled: bool


class ResolvedConfiguration(BaseModel):
    """
    The final resolved configuration for a given message.
    Hierarchy: message > session > workspace > global configuration
    """

    reasoning: ResolvedReasoningConfiguration
    peer_card: ResolvedPeerCardConfiguration
    summary: ResolvedSummaryConfiguration
    dream: ResolvedDreamConfiguration

    @model_validator(mode="before")
    @classmethod
    def migrate_deriver_to_reasoning(cls, data: Any) -> Any:
        """Handle v3.0.0 migration: 'deriver' was renamed to 'reasoning'."""
        if not isinstance(data, dict):
            return data

        config = cast(dict[str, Any], data)

        if "deriver" in config and "reasoning" not in config:
            config["reasoning"] = config.pop("deriver")

        return config


class PeerConfig(BaseModel):
    # TODO: Update description - should say "Whether honcho forms a representation of the peer itself"
    observe_me: bool | None = Field(
        default=None,
        description="Whether Honcho will use reasoning to form a representation of this peer",
    )


class SessionPeerConfig(PeerConfig):
    # TODO: Update description - should say "Whether this peer forms representations of other peers in the session"
    observe_others: bool | None = Field(
        default=None,
        description="Whether this peer should form a session-level theory-of-mind representation of other peers in the session",
    )

>sed -n '1,240p' src/utils/queue_payload.py

Chunk ID: 1e9d49
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1678
Output:
from datetime import datetime
from typing import Any, Literal

from pydantic import BaseModel, ConfigDict

from src.schemas import DreamType, ReconcilerType, ResolvedConfiguration


class BasePayload(BaseModel):
    """Base payload with common fields."""

    model_config = ConfigDict(extra="forbid")  # pyright: ignore[reportUnannotatedClassAttribute]


class RepresentationPayload(BasePayload):
    """Payload for representation tasks."""

    task_type: Literal["representation"] = "representation"
    session_name: str
    content: str
    observers: list[str]
    observed: str
    created_at: datetime
    configuration: ResolvedConfiguration


class RepresentationPayloads(BasePayload):
    """Payload for a batch of representation tasks."""

    payloads: list[RepresentationPayload]


class SummaryPayload(BasePayload):
    """Payload for summary tasks."""

    task_type: Literal["summary"] = "summary"
    session_name: str
    message_seq_in_session: int
    configuration: ResolvedConfiguration
    # Optional for backward compatibility with older queue items
    message_public_id: str | None = None


class WebhookPayload(BasePayload):
    """Payload for webhook delivery tasks."""

    task_type: Literal["webhook"] = "webhook"
    event_type: str
    data: dict[str, Any]


class DreamPayload(BasePayload):
    """Payload for dream tasks."""

    task_type: Literal["dream"] = "dream"
    dream_type: DreamType
    observer: str
    observed: str
    session_name: str | None = None


class DeletionPayload(BasePayload):
    """Payload for deletion tasks."""

    task_type: Literal["deletion"] = "deletion"
    deletion_type: Literal["session", "observation", "workspace"]
    resource_id: str


class ReconcilerPayload(BasePayload):
    """Payload for reconciler tasks (vector sync, queue cleanup, self-healing)."""

    task_type: Literal["reconciler"] = "reconciler"
    reconciler_type: ReconcilerType


def create_webhook_payload(
    event_type: str,
    data: dict[str, Any],
) -> dict[str, Any]:
    """Create a webhook payload."""
    return WebhookPayload(event_type=event_type, data=data).model_dump(
        mode="json", exclude_none=True
    )


def create_dream_payload(
    dream_type: DreamType,
    *,
    observer: str,
    observed: str,
    session_name: str | None = None,
) -> dict[str, Any]:
    """Create a dream payload."""
    return DreamPayload(
        dream_type=dream_type,
        observer=observer,
        observed=observed,
        session_name=session_name,
    ).model_dump(mode="json", exclude_none=True)


def create_deletion_payload(
    deletion_type: Literal["session", "observation", "workspace"],
    resource_id: str,
) -> dict[str, Any]:
    """Create a deletion payload."""
    return DeletionPayload(
        deletion_type=deletion_type,
        resource_id=resource_id,
    ).model_dump(mode="json", exclude_none=True)


def create_payload(
    message: dict[str, Any],
    configuration: ResolvedConfiguration,
    task_type: Literal["representation", "summary"],
    message_seq_in_session: int | None = None,
    *,
    observers: list[str] | None = None,
    observed: str | None = None,
) -> dict[str, Any]:
    """
    Create a processed payload from a message for queue processing.

    Note: workspace_name and message_id are no longer included in the returned payload
    as they are now stored in dedicated columns on the queue table. The caller is
    responsible for extracting and passing these values separately.

    Args:
        message: The original message dictionary
        task_type: Type of task ('representation' or 'summary')
        message_seq_in_session: Required for summary tasks, must be None for representation
        observers: List of observer peer names (required for representation tasks)
        observed: Name of the observed peer (*always* the peer who sent the message) (required for representation tasks)


    Returns:
        Processed payload dictionary ready for queue processing (without workspace_name and message_id)

    Raises:
        ValueError: If the payload doesn't match the expected schema
    """
    workspace_name = message.get("workspace_name")
    session_name = message.get("session_name")
    message_id = message.get("message_id")

    if not isinstance(workspace_name, str):
        raise TypeError("Workspace name must be a string")

    if not isinstance(session_name, str):
        raise TypeError("Session name must be a string")

    if not isinstance(message_id, int):
        raise TypeError("Message ID must be an integer")

    # Create the appropriate payload type based on task_type
    try:
        if task_type == "representation":
            content = message.get("content")
            created_at = message.get("created_at")

            if not isinstance(content, str):
                raise TypeError("Message content must be a string")

            if not isinstance(created_at, datetime):
                raise TypeError("created_at must be a datetime object")

            if observers is None or len(observers) == 0:
                raise ValueError("observers is required for representation tasks")

            if observed is None:
                raise ValueError("observed is required for representation tasks")

            validated_payload = RepresentationPayload(
                content=content,
                session_name=session_name,
                created_at=created_at,
                observers=observers,
                observed=observed,
                configuration=configuration,
            )
        elif task_type == "summary":
            if message_seq_in_session is None:
                raise ValueError("message_seq_in_session is required for summary tasks")
            message_public_id = message.get("message_public_id")
            if message_public_id is not None and (
                not isinstance(message_public_id, str) or not message_public_id.strip()
            ):
                raise ValueError(
                    "message_public_id must be a non-empty string if provided"
                )

            validated_payload = SummaryPayload(
                session_name=session_name,
                message_seq_in_session=message_seq_in_session,
                configuration=configuration,
                message_public_id=message_public_id,
            )

        # Convert back to dict for compatibility with JSON serialization
        # mode='json' ensures datetime is converted to ISO string
        payload = validated_payload.model_dump(mode="json", exclude_none=True)

    except Exception as e:
        raise ValueError(f"Failed to create valid payload: {str(e)}") from e

    return payload

>sed -n '280,620p' src/models.py

Chunk ID: 674ca7
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2840
Output:
    id: Mapped[int] = mapped_column(
        BigInteger, Identity(), primary_key=[REDACTED] autoincrement=True
    )
    content: Mapped[str] = mapped_column(TEXT)
    embedding: MappedColumn[Any] = mapped_column(Vector(_VECTOR_DIM), nullable=True)
    message_id: Mapped[str] = mapped_column(
        ForeignKey("messages.public_id", ondelete="CASCADE"), nullable=False, index=True
    )
    workspace_name: Mapped[str] = mapped_column(
        ForeignKey("workspaces.name"), nullable=False, index=True
    )
    session_name: Mapped[str] = mapped_column(TEXT, nullable=False, index=True)
    peer_name: Mapped[str] = mapped_column(TEXT, nullable=False, index=True)
    created_at: Mapped[datetime.datetime] = mapped_column(
        DateTime(timezone=True), server_default=func.now(), index=True
    )
    # Vector sync state tracking
    sync_state: Mapped[VectorSyncState] = mapped_column(
        TEXT, nullable=False, server_default="pending", index=True
    )
    last_sync_at: Mapped[datetime.datetime | None] = mapped_column(
        DateTime(timezone=True), nullable=True
    )
    sync_attempts: Mapped[int] = mapped_column(
        Integer, nullable=False, default=0, server_default=text("0")
    )

    __table_args__ = (
        # Compound foreign key constraints
        ForeignKeyConstraint(
            ["session_name", "workspace_name"],
            ["sessions.name", "sessions.workspace_name"],
        ),
        ForeignKeyConstraint(
            ["peer_name", "workspace_name"],
            ["peers.name", "peers.workspace_name"],
        ),
        # HNSW index on embedding column for efficient similarity search
        Index(
            "ix_message_embeddings_embedding_hnsw",
            "embedding",
            postgresql_using="hnsw",
            postgresql_with={"m": 16, "ef_construction": 64},
            postgresql_ops={"embedding": "vector_cosine_ops"},
        ),
        # Composite index for efficient reconciliation queries
        Index(
            "ix_message_embeddings_sync_state_last_sync_at",
            "sync_state",
            "last_sync_at",
        ),
    )


@final
class Collection(Base):
    __tablename__: str = "collections"

    id: Mapped[str] = mapped_column(TEXT, default=generate_nanoid, primary_key=[REDACTED]
    observer: Mapped[str] = mapped_column(TEXT, index=True)
    observed: Mapped[str] = mapped_column(TEXT, index=True)
    created_at: Mapped[datetime.datetime] = mapped_column(
        DateTime(timezone=True), server_default=func.now(), index=True
    )
    h_metadata: Mapped[dict[str, Any]] = mapped_column(
        "metadata", JSONB, default=dict, server_default=text("'{}'::jsonb")
    )
    internal_metadata: Mapped[dict[str, Any]] = mapped_column(
        "internal_metadata", JSONB, default=dict, server_default=text("'{}'::jsonb")
    )
    documents = relationship(
        "Document", back_populates="collection", cascade="all, delete, delete-orphan"
    )
    workspace_name: Mapped[str] = mapped_column(
        ForeignKey("workspaces.name"), nullable=False, index=True
    )

    __table_args__ = (
        UniqueConstraint(
            "observer",
            "observed",
            "workspace_name",
        ),
        CheckConstraint("length(id) = 21", name="id_length"),
        CheckConstraint("id ~ '^[A-Za-z0-9_-]+$'", name="id_format"),
        # Composite foreign key constraint for observer peer
        ForeignKeyConstraint(
            ["observer", "workspace_name"],
            ["peers.name", "peers.workspace_name"],
        ),
        # Composite foreign key constraint for observed peer
        ForeignKeyConstraint(
            ["observed", "workspace_name"],
            ["peers.name", "peers.workspace_name"],
        ),
    )


@final
class Document(Base):
    __tablename__: str = "documents"
    id: Mapped[str] = mapped_column(TEXT, default=generate_nanoid, primary_key=[REDACTED]
    internal_metadata: Mapped[dict[str, Any]] = mapped_column(
        "internal_metadata", JSONB, default=dict, server_default=text("'{}'::jsonb")
    )
    content: Mapped[str] = mapped_column(TEXT)
    level: Mapped[DocumentLevel] = mapped_column(
        TEXT, nullable=False, server_default="explicit"
    )
    times_derived: Mapped[int] = mapped_column(
        Integer, nullable=False, server_default=text("1")
    )
    embedding: MappedColumn[Any] = mapped_column(Vector(_VECTOR_DIM), nullable=True)
    source_ids: Mapped[list[str] | None] = mapped_column(
        JSONB, nullable=True, server_default=text("NULL")
    )
    created_at: Mapped[datetime.datetime] = mapped_column(
        DateTime(timezone=True), server_default=func.now(), index=True
    )

    observer: Mapped[str] = mapped_column(TEXT, index=True)
    observed: Mapped[str] = mapped_column(TEXT, index=True)
    workspace_name: Mapped[str] = mapped_column(
        ForeignKey("workspaces.name"), nullable=False, index=True
    )
    session_name: Mapped[str | None] = mapped_column(TEXT, nullable=True, index=True)
    deleted_at: Mapped[datetime.datetime | None] = mapped_column(
        DateTime(timezone=True), nullable=True, index=True, default=None
    )

    # Vector sync state tracking
    sync_state: Mapped[VectorSyncState] = mapped_column(
        TEXT, nullable=False, server_default="pending", index=True
    )
    last_sync_at: Mapped[datetime.datetime | None] = mapped_column(
        DateTime(timezone=True), nullable=True
    )
    sync_attempts: Mapped[int] = mapped_column(
        Integer, nullable=False, default=0, server_default=text("0")
    )

    collection = relationship("Collection", back_populates="documents")

    __table_args__ = (
        CheckConstraint("length(id) = 21", name="id_length"),
        CheckConstraint("length(content) <= 65535", name="content_length"),
        CheckConstraint("id ~ '^[A-Za-z0-9_-]+$'", name="id_format"),
        # Composite foreign key constraint for collections
        ForeignKeyConstraint(
            ["observer", "observed", "workspace_name"],
            [
                "collections.observer",
                "collections.observed",
                "collections.workspace_name",
            ],
        ),
        # Composite foreign key constraint for observer peer
        ForeignKeyConstraint(
            ["observer", "workspace_name"],
            ["peers.name", "peers.workspace_name"],
        ),
        # Composite foreign key constraint for observed peer
        ForeignKeyConstraint(
            ["observed", "workspace_name"],
            ["peers.name", "peers.workspace_name"],
        ),
        # Composite foreign key constraint for sessions
        ForeignKeyConstraint(
            ["session_name", "workspace_name"],
            ["sessions.name", "sessions.workspace_name"],
        ),
        # HNSW index on embedding column
        Index(
            "ix_documents_embedding_hnsw",
            "embedding",
            postgresql_using="hnsw",  # HNSW index type
            postgresql_with={"m": 16, "ef_construction": 64},  # HNSW parameters
            postgresql_ops={
                "embedding": "vector_cosine_ops"
            },  # Cosine distance operator
        ),
        # GIN index for efficient tree traversal (finding children by source IDs)
        Index(
            "ix_documents_source_ids_gin",
            "source_ids",
            postgresql_using="gin",
        ),
        # Composite index for efficient reconciliation queries
        Index(
            "ix_documents_sync_state_last_sync_at",
            "sync_state",
            "last_sync_at",
        ),
    )


@final
class QueueItem(Base):
    __tablename__: str = "queue"
    id: Mapped[int] = mapped_column(
        BigInteger, Identity(), primary_key=[REDACTED] autoincrement=True
    )
    session_id: Mapped[str | None] = mapped_column(
        ForeignKey("sessions.id"), nullable=True, index=True
    )
    work_unit_key: Mapped[str] = mapped_column(TEXT, nullable=False)

    task_type: Mapped[TaskType] = mapped_column(TEXT, nullable=False)
    payload: Mapped[dict[str, Any]] = mapped_column(JSONB, nullable=False)
    processed: Mapped[bool] = mapped_column(
        Boolean, default=False, server_default=text("false"), index=True
    )
    error: Mapped[str | None] = mapped_column(TEXT, nullable=True)
    created_at: Mapped[datetime.datetime] = mapped_column(
        DateTime(timezone=True), server_default=func.now(), index=True
    )
    workspace_name: Mapped[str | None] = mapped_column(
        ForeignKey("workspaces.name"), nullable=True, index=True
    )
    message_id: Mapped[int | None] = mapped_column(
        BigInteger, ForeignKey("messages.id"), nullable=True
    )

    __table_args__ = (
        Index(
            "ix_queue_message_id_not_null",
            "message_id",
            postgresql_where=text("message_id IS NOT NULL"),
        ),
        Index(
            "ix_queue_work_unit_key_processed_id",
            "work_unit_key",
            "processed",
            "id",
        ),
        # Partial unique index for reconciler task deduplication
        Index(
            "uq_queue_reconciler_pending_work_unit_key",
            "work_unit_key",
            unique=True,
            postgresql_where=text("task_type = 'reconciler' AND processed = false"),
        ),
        # Partial unique index for dream task deduplication
        Index(
            "uq_queue_dream_pending_work_unit_key",
            "work_unit_key",
            unique=True,
            postgresql_where=text("task_type = 'dream' AND processed = false"),
        ),
    )

    def __repr__(self) -> str:
        return f"QueueItem(id={self.id}, session_id={self.session_id}, work_unit_key=[REDACTED] task_type={self.task_type}, payload={self.payload}, processed={self.processed}, workspace_name={self.workspace_name}, message_id={self.message_id})"


@final
class ActiveQueueSession(Base):
    __tablename__: str = "active_queue_sessions"

    id: Mapped[str] = mapped_column(TEXT, default=generate_nanoid, primary_key=[REDACTED]

    work_unit_key: Mapped[str] = mapped_column(TEXT, unique=True)

    last_updated: Mapped[datetime.datetime] = mapped_column(
        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
    )


@final
class WebhookEndpoint(Base):
    __tablename__: str = "webhook_endpoints"
    id: Mapped[str] = mapped_column(TEXT, default=generate_nanoid, primary_key=[REDACTED]
    workspace_name: Mapped[str] = mapped_column(
        ForeignKey("workspaces.name"), nullable=False, index=True
    )
    url: Mapped[str] = mapped_column(TEXT, nullable=False)
    created_at: Mapped[datetime.datetime] = mapped_column(
        DateTime(timezone=True), server_default=func.now()
    )

    workspace = relationship("Workspace", back_populates="webhook_endpoints")

    __table_args__ = (CheckConstraint("length(url) <= 2048", name="url_length"),)

    def __repr__(self) -> str:
        return f"WebhookEndpoint(id={self.id}, workspace_name={self.workspace_name}, url={self.url})"


@final
class SessionPeer(Base):
    __table__: Table = session_peers_table

    # Type annotations for the columns
    workspace_name: Mapped[str]
    session_name: Mapped[str]
    peer_name: Mapped[str]
    configuration: Mapped[dict[str, Any]]
    internal_metadata: Mapped[dict[str, Any]]
    joined_at: Mapped[datetime.datetime]
    left_at: Mapped[datetime.datetime | None]

>sed -n '240,520p' src/utils/representation.py

Chunk ID: 390b27
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2868
Output:
    """Contradiction observation - notes when user has made conflicting statements, plus metadata."""

    def __str__(self) -> str:
        sources_text = ""
        if self.sources:
            source_lines = [f"    - {source}" for source in self.sources]
            sources_text = "\n" + "\n".join(source_lines)
        return f"[{_strip_microseconds_and_timezone(self.created_at)}] CONTRADICTION: {self.content}{sources_text}"

    def str_with_id(self) -> str:
        """Format with ID prefix for use by agents that need to reference observations."""
        id_prefix = f"[id:{self.id}] " if self.id else ""
        sources_text = ""
        if self.sources:
            source_lines = [f"    - {source}" for source in self.sources]
            sources_text = "\n" + "\n".join(source_lines)
        return f"{id_prefix}[{_strip_microseconds_and_timezone(self.created_at)}] CONTRADICTION: {self.content}{sources_text}"

    def str_no_timestamps(self) -> str:
        sources_text = ""
        if self.sources:
            source_lines = [f"    - {source}" for source in self.sources]
            sources_text = "\n" + "\n".join(source_lines)
        return f"CONTRADICTION: {self.content}{sources_text}"

    def __hash__(self) -> int:
        """Make ContradictionObservation hashable for use in sets."""
        return hash((self.content, self.created_at, self.session_name))

    def __eq__(self, other: object) -> bool:
        """Define equality for ContradictionObservation objects."""
        if not isinstance(other, ContradictionObservation):
            return False
        return (
            self.content == other.content
            and self.created_at == other.created_at
            and self.session_name == other.session_name
        )


class Representation(BaseModel):
    """
    A Representation is a traversable and diffable map of observations.
    At the base, we have a list of explicit observations, derived from a peer's messages.

    From there, deductive observations can be made by establishing logical relationships between explicit observations.

    In the future, we can add more levels of reasoning on top of these.

    All of a peer's observations are stored as documents in a collection. These documents can be queried in various ways
    to produce this Representation object.

    Additionally, a "working representation" is a version of this data structure representing the most recent observations
    within a single session.

    A representation can have a maximum number of observations, which is applied individually to each level of reasoning.
    If a maximum is set, observations are added and removed in FIFO order.
    """

    explicit: list[ExplicitObservation] = Field(
        description="Facts LITERALLY stated by the user - direct quotes or clear paraphrases only, no interpretation or inference. Example: ['The user is 25 years old', 'The user has a dog']",
        default_factory=list,
    )
    deductive: list[DeductiveObservation] = Field(
        description="Conclusions that MUST be true given explicit facts and premises - strict logical necessities. Each deduction should have premises and a single conclusion.",
        default_factory=list,
    )
    inductive: list[InductiveObservation] = Field(
        description="Patterns, generalizations, and personality insights inferred from multiple observations. Higher-level reasoning created by the Dreamer agent.",
        default_factory=list,
    )
    contradiction: list[ContradictionObservation] = Field(
        description="Conflicting statements made by the user that need clarification. The dialectic agent should surface these when relevant.",
        default_factory=list,
    )

    def is_empty(self) -> bool:
        """
        Check if the representation is empty.
        """
        return (
            len(self.explicit) == 0
            and len(self.deductive) == 0
            and len(self.inductive) == 0
            and len(self.contradiction) == 0
        )

    def len(self) -> int:
        """
        Return the total number of observations in the representation.
        """
        return (
            len(self.explicit)
            + len(self.deductive)
            + len(self.inductive)
            + len(self.contradiction)
        )

    def diff_representation(self, other: "Representation") -> "Representation":
        """
        Given this and another representation, return a new representation with only observations that are unique to the other.
        Note that this only removes literal duplicates, not semantically equivalent ones.
        """
        diff = Representation()
        diff.explicit = [o for o in other.explicit if o not in self.explicit]
        diff.deductive = [o for o in other.deductive if o not in self.deductive]
        diff.inductive = [o for o in other.inductive if o not in self.inductive]
        diff.contradiction = [
            o for o in other.contradiction if o not in self.contradiction
        ]
        return diff

    def merge_representation(
        self, other: "Representation", max_observations: int | None = None
    ):
        """
        Merge another representation object into this one.
        This will automatically deduplicate explicit, deductive, inductive, and contradiction observations.
        This *preserves order* of observations so that they retain FIFO order.

        NOTE: observations with the *same* timestamp will not have order preserved.
        That's fine though, because they are from the same timestamp...
        """
        # removing duplicates by going list->set->list
        self.explicit = list(set(self.explicit + other.explicit))
        self.deductive = list(set(self.deductive + other.deductive))
        self.inductive = list(set(self.inductive + other.inductive))
        self.contradiction = list(set(self.contradiction + other.contradiction))
        # sort by created_at
        self.explicit.sort(key=lambda x: x.created_at)
        self.deductive.sort(key=lambda x: x.created_at)
        self.inductive.sort(key=lambda x: x.created_at)
        self.contradiction.sort(key=lambda x: x.created_at)

        if max_observations:
            self.explicit = self.explicit[-max_observations:]
            self.deductive = self.deductive[-max_observations:]
            self.inductive = self.inductive[-max_observations:]
            self.contradiction = self.contradiction[-max_observations:]

    def __str__(self) -> str:
        """
        Format representation into a clean, readable string for LLM prompts.
        NOTE: we always strip subsecond precision from the timestamps.

        Returns:
            Formatted string with clear sections and bullet points including temporal metadata
            Example:
            EXPLICIT:
            1. [2025-01-01 12:00:00] The user has a dog named Rover
            2. [2025-01-01 12:01:00] The user's dog is 5 years old
            3. [2025-01-01 12:05:00] The user is 25 years old
            DEDUCTIVE:
            1. [2025-01-01 12:01:00] Rover is 5 years old
                - The user has a dog named Rover
                - The user's dog is 5 years old

        """

        parts: list[str] = []

        parts.append("EXPLICIT:\n")
        for i, observation in enumerate(self.explicit, 1):
            parts.append(f"{i}. {observation}")
        parts.append("")

        parts.append("DEDUCTIVE:\n")
        for i, observation in enumerate(self.deductive, 1):
            parts.append(f"{i}. {observation}")
        parts.append("")

        parts.append("INDUCTIVE:\n")
        for i, observation in enumerate(self.inductive, 1):
            parts.append(f"{i}. {observation}")
        parts.append("")

        parts.append("CONTRADICTION:\n")
        for i, observation in enumerate(self.contradiction, 1):
            parts.append(f"{i}. {observation}")
        parts.append("")

        return "\n".join(parts)

    def str_with_ids(self) -> str:
        """
        Format representation with observation IDs for agents that need to reference/delete observations.

        Returns:
            Formatted string with IDs included
            Example:
            EXPLICIT:
            1. [id:abc123] [2025-01-01 12:00:00] The user has a dog named Rover
            2. [id:def456] [2025-01-01 12:01:00] The user's dog is 5 years old
            DEDUCTIVE:
            1. [id:ghi789] [2025-01-01 12:01:00] Rover is 5 years old
                - The user has a dog named Rover
                - The user's dog is 5 years old
            INDUCTIVE:
            1. [id:jkl012] [2025-01-01 12:05:00] [high] User tends to be methodical
                - id:abc123
                - id:def456
        """
        parts: list[str] = []

        parts.append("EXPLICIT:\n")
        for i, observation in enumerate(self.explicit, 1):
            parts.append(f"{i}. {observation.str_with_id()}")
        parts.append("")

        parts.append("DEDUCTIVE:\n")
        for i, observation in enumerate(self.deductive, 1):
            parts.append(f"{i}. {observation.str_with_id()}")
        parts.append("")

        parts.append("INDUCTIVE:\n")
        for i, observation in enumerate(self.inductive, 1):
            parts.append(f"{i}. {observation.str_with_id()}")
        parts.append("")

        parts.append("CONTRADICTION:\n")
        for i, observation in enumerate(self.contradiction, 1):
            parts.append(f"{i}. {observation.str_with_id()}")
        parts.append("")

        return "\n".join(parts)

    def str_no_timestamps(self) -> str:
        """
        Format representation into a clean, readable string for LLM prompts... but without timestamps.

        Returns:
            Formatted string with clear sections and bullet points including temporal metadata
            Example:
            EXPLICIT:
            1. The user has a dog named Rover
            2. The user's dog is 5 years old
            3. The user is 25 years old
            DEDUCTIVE:
            1. Rover is 5 years old
                - The user has a dog named Rover
                - The user's dog is 5 years old
            INDUCTIVE:
            1. [high] User tends to be methodical
                - id:abc123
                - id:def456

        """
        parts: list[str] = []

        parts.append("EXPLICIT:\n")
        for i, observation in enumerate(self.explicit, 1):
            parts.append(f"{i}. {observation.content}")
        parts.append("")

        parts.append("DEDUCTIVE:\n")
        for i, observation in enumerate(self.deductive, 1):
            parts.append(f"{i}. {observation.str_no_timestamps()}")
        parts.append("")

        parts.append("INDUCTIVE:\n")
        for i, observation in enumerate(self.inductive, 1):
            parts.append(f"{i}. {observation.str_no_timestamps()}")
        parts.append("")

        parts.append("CONTRADICTION:\n")
        for i, observation in enumerate(self.contradiction, 1):
            parts.append(f"{i}. {observation.str_no_timestamps()}")
        parts.append("")

        return "\n".join(parts)

    def format_as_markdown(self, include_ids: bool = False) -> str:
        """
        Format a Representation object as markdown.
        NOTE: we always strip subsecond precision from the timestamps.

        Args:
            include_ids: If True, include observation IDs for use with get_reasoning_chain

        Returns:
            Formatted markdown string

>sed -n '1,320p' src/deriver/enqueue.py

Chunk ID: 5a8069
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2688
Output:
import logging
from typing import Any, Literal

from sqlalchemy import exists, insert, select
from sqlalchemy.ext.asyncio import AsyncSession

from src import crud, models, schemas
from src.config import settings
from src.dependencies import tracked_db
from src.dreamer.dream_scheduler import get_dream_scheduler
from src.exceptions import ValidationException
from src.models import QueueItem
from src.schemas import MessageConfiguration, ResolvedConfiguration
from src.utils.config_helpers import get_configuration
from src.utils.queue_payload import (
    create_deletion_payload,
    create_dream_payload,
    create_payload,
)
from src.utils.work_unit import construct_work_unit_key

logger = logging.getLogger(__name__)


async def enqueue(payload: list[dict[str, Any]]) -> None:
    """
    Add message(s) to the deriver queue for processing.

    Args:
        payload: List of message payload dictionaries
    """

    # Cancel any pending dreams for affected collections since user is active again.
    # This cancels dreams for all collections where observed=peer_name, which covers
    # both self-observation and peer-to-peer observation cases.
    dream_scheduler = get_dream_scheduler()
    if dream_scheduler and payload:
        cancelled_dreams: set[str] = set()
        for message in payload:
            workspace_name = message.get("workspace_name")
            peer_name = message.get("peer_name")
            if workspace_name and peer_name:
                cancelled = await dream_scheduler.cancel_dreams_for_observed(
                    workspace_name, peer_name
                )
                cancelled_dreams.update(cancelled)

        if cancelled_dreams:
            logger.info(
                f"Cancelled {len(cancelled_dreams)} pending dreams due to new activity"
            )

    async with tracked_db("message_enqueue") as db_session:
        try:
            # Determine if batch or single processing
            if not payload:  # Empty list check
                return
            workspace_name = payload[0]["workspace_name"]
            session_name = payload[0]["session_name"]

            if session_name is None or workspace_name is None:
                raise ValidationException("Session and workspace are required")

            queue_records = await handle_session(
                db_session, payload, workspace_name, session_name
            )

            if queue_records:
                stmt = insert(QueueItem).returning(QueueItem)
                await db_session.execute(stmt, queue_records)
                await db_session.commit()

        except Exception as e:
            logger.exception("Failed to enqueue message(s)!")
            if settings.SENTRY.ENABLED:
                import sentry_sdk

                sentry_sdk.capture_exception(e)


async def handle_session(
    db_session: AsyncSession,
    payload: list[dict[str, Any]],
    workspace_name: str,
    session_name: str,
) -> list[dict[str, Any]]:
    """
    Handle enqueueing for normal session cases, creating appropriate queue items based on configurations.

    Args:
        db_session: The database session
        payload: List of message payloads
        workspace_name: Name of the workspace
        session_name: Name of the session

    Returns:
        List of queue records to insert
    """
    session = (
        await crud.get_or_create_session(
            db_session,
            session=schemas.SessionCreate(name=session_name),
            workspace_name=workspace_name,
        )
    ).resource

    # Fetch workspace for configuration resolution
    workspace = await crud.get_workspace(db_session, workspace_name=workspace_name)

    # Resolve summary configuration with hierarchical fallback
    session_level_configuration = get_configuration(None, session, workspace)

    peers_with_configuration = await get_peers_with_configuration(
        db_session, workspace_name, session_name
    )

    queue_records: list[dict[str, Any]] = []

    for message in payload:
        message_config: MessageConfiguration | None = message.get("configuration")
        if message_config is not None:
            message_level_configuration = get_configuration(
                message_config, session, workspace
            )
        else:
            message_level_configuration = session_level_configuration
        queue_records.extend(
            await generate_queue_records(
                db_session,
                message,
                peers_with_configuration,
                session.id,
                message_level_configuration,
            )
        )
    return queue_records


async def get_peers_with_configuration(
    db_session: AsyncSession, workspace_name: str, session_name: str
) -> dict[str, list[dict[str, Any]]]:
    """
    Retrieve peers with their configurations for a given session.

    Args:
        db_session: The database session
        workspace_name: Name of the workspace
        session_name: Name of the session

    Returns:
        Dictionary mapping peer names to their configurations
    """
    configuration_query = await crud.get_session_peer_configuration(
        workspace_name=workspace_name, session_name=session_name
    )
    peers_with_configuration_result = await db_session.execute(configuration_query)
    peers_with_configuration_list = peers_with_configuration_result.all()
    return {
        row.peer_name: [
            row.peer_configuration,
            row.session_peer_configuration,
            row.is_active,
        ]
        for row in peers_with_configuration_list
    }


def create_representation_record(
    message: dict[str, Any],
    conf: ResolvedConfiguration,
    session_id: str | None = None,
    *,
    observers: list[str],
    observed: str,
) -> dict[str, Any]:
    """
    Create a queue record for representation task.

    Args:
        message: The message payload
        conf: Resolved configuration for this particular message
        session_id: Optional session ID
        observers: List of observer peer names
        observed: Name of the sender

    Returns:
        Queue record dictionary with workspace_name and message_id as separate fields
    """
    workspace_name = message.get("workspace_name")
    message_id = message.get("message_id")

    if not isinstance(workspace_name, str):
        raise TypeError("workspace_name is required and must be a string")
    if not isinstance(message_id, int):
        raise TypeError("message_id is required and must be an integer")

    processed_payload: dict[str, Any] = create_payload(
        message=message,
        configuration=conf,
        task_type="representation",
        observers=observers,
        observed=observed,
    )
    return {
        "work_unit_key": construct_work_unit_key(workspace_name, processed_payload),
        "payload": processed_payload,
        "session_id": session_id,
        "task_type": "representation",
        "workspace_name": workspace_name,
        "message_id": message_id,
    }


def create_summary_record(
    message: dict[str, Any],
    configuration: ResolvedConfiguration,
    session_id: str,
    message_seq_in_session: int,
) -> dict[str, Any]:
    """
    Create a queue record for summary task.

    Args:
        message: The message payload
        session_id: Session ID
        message_seq_in_session: The sequence number of the message in the session

    Returns:
        Queue record dictionary with workspace_name and message_id as separate fields
    """
    workspace_name = message.get("workspace_name")
    message_id = message.get("message_id")

    if not isinstance(workspace_name, str):
        raise ValueError("workspace_name is required and must be a string")
    if not isinstance(message_id, int):
        raise ValueError("message_id is required and must be an integer")

    processed_payload = create_payload(
        message=message,
        configuration=configuration,
        task_type="summary",
        message_seq_in_session=message_seq_in_session,
    )
    return {
        "work_unit_key": construct_work_unit_key(workspace_name, processed_payload),
        "payload": processed_payload,
        "session_id": session_id,
        "task_type": "summary",
        "workspace_name": workspace_name,
        "message_id": message_id,
    }


def get_effective_observe_me(
    observed: str, peers_with_configuration: dict[str, list[dict[str, Any]]]
) -> bool:
    """
    Determine the effective observe_me setting for a sender, considering session and peer configurations.

    Args:
        observed: Name of the sender
        peers_with_configuration: Dictionary of peer configurations

    Returns:
        True if observe_me is enabled, False otherwise
    """
    # If the sender is not in peers_with_configuration, they left after sending a message.
    # We'll use the default behavior of observing the sender by instantiating the default
    # peer-level and session-level configs.
    configuration: list[Any] = peers_with_configuration.get(observed, [{}, {}])
    sender_session_peer_config = (
        schemas.SessionPeerConfig(**configuration[1]) if configuration[1] else None
    )
    sender_peer_config = (
        schemas.PeerConfig(**configuration[0])
        if configuration[0]
        else schemas.PeerConfig()
    )

    # Session peer config takes precedence if it exists and has observe_me set
    if sender_session_peer_config and sender_session_peer_config.observe_me is not None:
        return sender_session_peer_config.observe_me

    # Otherwise use peer config
    return (
        sender_peer_config.observe_me
        if sender_peer_config.observe_me is not None
        else True
    )


async def generate_queue_records(
    db_session: AsyncSession,
    message: dict[str, Any],
    peers_with_configuration: dict[str, list[dict[str, Any]]],
    session_id: str,
    conf: ResolvedConfiguration,
) -> list[dict[str, Any]]:
    """
    Process a single message and generate queue records based on configurations.

    Args:
        db_session: The database session
        message: The message payload
        peers_with_configuration: Dictionary of peer configurations
        session_id: Session ID
        configuration: Resolved configuration for this particular message

    Returns:
        List of queue records for this message
    """
    observed = message["peer_name"]
    message_id: int = message["message_id"]

    # Prefer the sequence captured during message creation; fallback only if missing
    message_seq_in_session = int(message.get("seq_in_session") or 0)
    if message_seq_in_session <= 0:
        message_seq_in_session = await crud.get_message_seq_in_session(
            db_session,

>sed -n '1,320p' src/deriver/deriver.py

Chunk ID: d5d11b
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2246
Output:
import logging
import time

from src import crud
from src.config import ConfiguredModelSettings, settings
from src.crud.representation import RepresentationManager
from src.dependencies import tracked_db
from src.llm import honcho_llm_call
from src.models import Message
from src.schemas import ResolvedConfiguration
from src.telemetry import prometheus_metrics
from src.telemetry.events import RepresentationCompletedEvent, emit
from src.telemetry.logging import accumulate_metric, log_performance_metrics
from src.telemetry.prometheus.metrics import (
    DeriverComponents,
    DeriverTaskTypes,
    TokenTypes,
)
from src.telemetry.sentry import with_sentry_transaction
from src.utils.config_helpers import get_configuration
from src.utils.formatting import format_new_turn_with_timestamp
from src.utils.representation import PromptRepresentation, Representation
from src.utils.tokens import track_deriver_input_tokens

from .prompts import estimate_deriver_prompt_tokens, minimal_deriver_prompt

logger = logging.getLogger(__name__)


def _get_deriver_model_config() -> ConfiguredModelSettings:
    return settings.DERIVER.MODEL_CONFIG


@with_sentry_transaction("minimal_deriver_batch", op="deriver")
async def process_representation_tasks_batch(
    messages: list[Message],
    message_level_configuration: ResolvedConfiguration | None,
    *,
    observers: list[str],
    observed: str,
    queue_item_message_ids: list[int],
) -> None:
    """
    Process messages with minimal overhead - single LLM call, save to multiple collections.

    Args:
        messages: List of messages to process (includes interleaving context).
        message_level_configuration: Optional configuration override.
        observers: List of observer peer IDs (collections to save to).
        observed: The observed peer ID.
        queue_item_message_ids: Message IDs from queue items being processed
    """
    if not messages:
        return

    overall_start = time.perf_counter()

    messages.sort(key=lambda x: x.id)
    latest_message = messages[-1]
    earliest_message = messages[0]

    # Get configuration if not provided
    # TODO: this appears to be a very rare edge case coming out of `get_queue_item_batch` in queue_manager.py,
    # possible that we can remove this and require configuration to come through with the payload.
    if message_level_configuration is None:
        async with tracked_db("minimal_deriver.get_config") as db:
            message_level_configuration = get_configuration(
                None,
                await crud.get_session(
                    db, latest_message.session_name, latest_message.workspace_name
                ),
                await crud.get_workspace(
                    db, workspace_name=latest_message.workspace_name
                ),
            )

    # Skip if disabled
    if message_level_configuration.reasoning.enabled is False:
        return

    custom_instructions = message_level_configuration.reasoning.custom_instructions

    accumulate_metric(
        f"minimal_deriver_{latest_message.id}_{observed}",
        "starting_message_id",
        earliest_message.id,
        "id",
    )
    accumulate_metric(
        f"minimal_deriver_{latest_message.id}_{observed}",
        "ending_message_id",
        latest_message.id,
        "id",
    )

    # Format messages with timestamps
    formatted_messages = "\n".join(
        format_new_turn_with_timestamp(msg.content, msg.created_at, msg.peer_name)
        for msg in messages
    )

    # Track token usage - count only tokens from messages being processed
    prompt_tokens = estimate_deriver_prompt_tokens(custom_instructions)
    queue_item_message_ids_set = set(queue_item_message_ids)
    messages_tokens = sum(
        msg.token_count for msg in messages if msg.id in queue_item_message_ids_set
    )
    track_deriver_input_tokens(
        task_type=DeriverTaskTypes.INGESTION,
        components={
            DeriverComponents.PROMPT: prompt_tokens,
            DeriverComponents.MESSAGES: messages_tokens,
        },
    )

    # Build prompt
    prompt = minimal_deriver_prompt(
        peer_id=observed,
        messages=formatted_messages,
        custom_instructions=custom_instructions,
    )

    context_prep_duration = (time.perf_counter() - overall_start) * 1000
    accumulate_metric(
        f"minimal_deriver_{latest_message.id}_{observed}",
        "context_preparation",
        context_prep_duration,
        "ms",
    )

    # validation on settings means max_tokens will always be > 0
    base_model_config = _get_deriver_model_config()
    max_tokens = base_model_config.max_output_tokens or settings.LLM.DEFAULT_MAX_TOKENS
    model_config = base_model_config

    # Single LLM call
    llm_start = time.perf_counter()
    response = await honcho_llm_call(
        model_config=model_config,
        prompt=prompt,
        max_tokens=[REDACTED]
        track_name="Minimal Deriver",
        response_model=PromptRepresentation,
        json_mode=True,
        max_input_tokens=[REDACTED]
        enable_retry=True,
        retry_attempts=3,
        trace_name="minimal_deriver",
    )
    llm_duration = (time.perf_counter() - llm_start) * 1000

    accumulate_metric(
        f"minimal_deriver_{latest_message.id}_{observed}",
        "llm_call_duration",
        llm_duration,
        "ms",
    )

    # Prometheus metrics
    if settings.METRICS.ENABLED:
        prometheus_metrics.record_deriver_tokens(
            count=response.output_tokens,
            task_type=DeriverTaskTypes.INGESTION.value,
            token_type=[REDACTED]
            component=DeriverComponents.OUTPUT_TOTAL.value,
        )

    message_ids = [m.id for m in messages if m.peer_name == observed]

    # Convert to Representation and save
    observations = Representation.from_prompt_representation(
        response.content,
        message_ids,
        latest_message.session_name,
        latest_message.created_at,
    )

    if observations.is_empty() or not message_ids:
        logger.warning(
            "Deriver generated zero observations for messages %s:%s in %s/%s!",
            earliest_message.id,
            latest_message.id,
            latest_message.workspace_name,
            latest_message.session_name,
        )
    else:
        # Save to all observer collections
        for observer in observers:
            representation_manager = RepresentationManager(
                workspace_name=latest_message.workspace_name,
                observer=observer,
                observed=observed,
            )

            try:
                await representation_manager.save_representation(
                    observations,
                    message_ids,
                    latest_message.session_name,
                    latest_message.created_at,
                    message_level_configuration,
                )
            except Exception as e:
                logger.error(
                    "Failed to save representation for observer %s: %s", observer, e
                )

    # Log metrics
    overall_duration = (time.perf_counter() - overall_start) * 1000
    accumulate_metric(
        f"minimal_deriver_{latest_message.id}_{observed}",
        "total_processing_time",
        overall_duration,
        "ms",
    )

    total_observations = len(observations.explicit) + len(observations.deductive)
    accumulate_metric(
        f"minimal_deriver_{latest_message.id}_{observed}",
        "observation_count",
        total_observations,
        "count",
    )

    if settings.DERIVER.LOG_OBSERVATIONS:
        # Log messages fed into deriver
        accumulate_metric(
            f"minimal_deriver_{latest_message.id}_{observed}",
            "messages",
            formatted_messages,
            "blob",
        )
        # Log actual observations created as blob metrics
        accumulate_metric(
            f"minimal_deriver_{latest_message.id}_{observed}",
            "explicit_observations",
            "\n".join(f"  • {obs}" for obs in observations.explicit),
            "blob",
        )

    log_performance_metrics("minimal_deriver", f"{latest_message.id}_{observed}")

    # Emit telemetry event
    emit(
        RepresentationCompletedEvent(
            workspace_name=latest_message.workspace_name,
            session_name=latest_message.session_name,
            observed=observed,
            queue_items_processed=len(queue_item_message_ids),
            earliest_message_id=earliest_message.public_id,
            latest_message_id=latest_message.public_id,
            message_count=len(messages),
            explicit_conclusion_count=len(observations.explicit),
            context_preparation_ms=context_prep_duration,
            llm_call_ms=llm_duration,
            total_duration_ms=overall_duration,
            input_tokens=[REDACTED]
            output_tokens=[REDACTED]
        )
    )

>sed -n '320,760p' src/deriver/enqueue.py

Chunk ID: 2e24cc
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2300
Output:
            db_session,
            workspace_name=message["workspace_name"],
            session_name=message["session_name"],
            message_id=message_id,
        )

    records: list[dict[str, Any]] = []

    if conf.summary.enabled and (
        message_seq_in_session % conf.summary.messages_per_short_summary == 0
        or message_seq_in_session % conf.summary.messages_per_long_summary == 0
    ):
        records.append(
            create_summary_record(
                message,
                configuration=conf,
                session_id=session_id,
                message_seq_in_session=message_seq_in_session,
            )
        )

    # Check if the sender should be observed based on peer configuration
    should_observe = get_effective_observe_me(observed, peers_with_configuration)

    if not conf.reasoning.enabled:
        return records

    # Collect all observers into a single list
    observers: list[str] = []

    if should_observe:
        # Self-observation: the sender observes themselves
        observers.append(observed)

        # Other peers who want to observe
        for peer_name, peer_conf in peers_with_configuration.items():
            if peer_name == observed:
                continue

            # If the observer peer has left the session, skip them
            if not peer_conf[2]:
                continue

            session_peer_config = (
                schemas.SessionPeerConfig(**peer_conf[1]) if peer_conf[1] else None
            )

            if session_peer_config is None or not session_peer_config.observe_others:
                continue

            observers.append(peer_name)

    # Create a single record with all observers (if any)
    if observers:
        records.append(
            create_representation_record(
                message,
                conf,
                observed=observed,
                observers=observers,
                session_id=session_id,
            )
        )

    logger.debug(
        "message %s from %s created %s queue items with %s observers",
        message_id,
        observed,
        len(records),
        len(observers),
    )

    return records


def create_dream_record(
    workspace_name: str,
    *,
    observer: str,
    observed: str,
    dream_type: schemas.DreamType,
    session_name: str | None = None,
) -> dict[str, Any]:
    """
    Create a queue record for a dream task.

    Args:
        workspace_name: Name of the workspace
        observer: Name of the observer peer
        observed: Name of the observed peer
        dream_type: Type of dream to execute
        session_name: Name of the session to scope the dream to if specified

    Returns:
        Queue record dictionary with workspace_name and other fields
    """
    dream_payload = create_dream_payload(
        dream_type,
        observer=observer,
        observed=observed,
        session_name=session_name,
    )

    return {
        "work_unit_key": construct_work_unit_key(workspace_name, dream_payload),
        "payload": dream_payload,
        "session_id": None,
        "task_type": "dream",
        "workspace_name": workspace_name,
        "message_id": None,
    }


async def enqueue_dream(
    workspace_name: str,
    observer: str,
    observed: str,
    dream_type: schemas.DreamType,
    session_name: str | None = None,
) -> None:
    """
    Enqueue a dream task for immediate processing by the deriver.

    Does not touch collection.internal_metadata["dream"] — both guard fields
    are written atomically in process_dream on successful completion.

    Deduplication: If a dream with the same work_unit_key is already in-progress
    (has an ActiveQueueSession) or pending in the queue, the enqueue is skipped.

    Args:
        workspace_name: Name of the workspace
        observer: Name of the observer peer
        observed: Name of the observed peer
        dream_type: Type of dream to execute
        session_name: Name of the session to scope the dream to if specified
    """
    async with tracked_db("dream_enqueue") as db_session:
        try:
            dream_record = create_dream_record(
                workspace_name,
                observer=observer,
                observed=observed,
                dream_type=dream_type,
                session_name=session_name,
            )

            work_unit_key = dream_record["work_unit_key"]

            in_progress_check = select(
                exists(
                    select(models.ActiveQueueSession.id).where(
                        models.ActiveQueueSession.work_unit_key == work_unit_key
                    )
                )
            )
            is_in_progress = await db_session.scalar(in_progress_check)

            if is_in_progress:
                logger.info(
                    "Skipping dream enqueue - already in progress: %s/%s/%s (type: %s)",
                    workspace_name,
                    observer,
                    observed,
                    dream_type.value,
                )
                return

            pending_check = select(
                exists(
                    select(QueueItem.id).where(
                        QueueItem.work_unit_key == work_unit_key,
                        QueueItem.processed == False,  # noqa: E712
                    )
                )
            )
            is_pending = await db_session.scalar(pending_check)

            if is_pending:
                logger.info(
                    "Dream already pending in queue: %s/%s/%s (type: %s)",
                    workspace_name,
                    observer,
                    observed,
                    dream_type.value,
                )
                return

            stmt = insert(QueueItem).returning(QueueItem)
            await db_session.execute(stmt, [dream_record])
            await db_session.commit()

            logger.info(
                "Enqueued dream task for %s/%s/%s (type: %s)",
                workspace_name,
                observer,
                observed,
                dream_type.value,
            )

        except Exception as e:
            logger.exception("Failed to enqueue dream task!")
            if settings.SENTRY.ENABLED:
                import sentry_sdk

                sentry_sdk.capture_exception(e)
            raise


def create_deletion_record(
    workspace_name: str,
    deletion_type: Literal["session", "observation", "workspace"],
    resource_id: str,
) -> dict[str, Any]:
    """
    Create a queue record for a deletion task.

    Args:
        workspace_name: Name of the workspace
        deletion_type: Type of resource to delete ("session" or "observation")
        resource_id: ID of the resource to delete

    Returns:
        Queue record dictionary for insertion into the queue
    """
    deletion_payload = create_deletion_payload(
        deletion_type=deletion_type,
        resource_id=resource_id,
    )

    return {
        "work_unit_key": construct_work_unit_key(workspace_name, deletion_payload),
        "payload": deletion_payload,
        "session_id": None,
        "task_type": "deletion",
        "workspace_name": workspace_name,
        "message_id": None,
    }


async def enqueue_deletion(
    workspace_name: str,
    deletion_type: Literal["session", "observation", "workspace"],
    resource_id: str,
    db_session: AsyncSession | None = None,
) -> None:
    """
    Enqueue a deletion task for processing by the deriver.

    This function adds a deletion task to the queue for asynchronous processing.
    The deletion will be handled by the queue consumer with retry support.

    Args:
        workspace_name: Name of the workspace
        deletion_type: Type of resource to delete ("session" or "observation")
        resource_id: ID of the resource to delete
        db_session: Optional database session. If provided, uses this session
            instead of creating a new one. The caller is responsible for committing.
    """

    async def _do_enqueue(session: AsyncSession, should_commit: bool) -> None:
        deletion_record = create_deletion_record(
            workspace_name,
            deletion_type,
            resource_id,
        )

        stmt = insert(QueueItem).returning(QueueItem)
        await session.execute(stmt, [deletion_record])

        if should_commit:
            await session.commit()

        logger.info(
            "Enqueued deletion task: type=%s, resource_id=%s, workspace=%s",
            deletion_type,
            resource_id,
            workspace_name,
        )

    try:
        if db_session is not None:
            # Use the provided session - caller is responsible for committing
            await _do_enqueue(db_session, should_commit=False)
        else:
            # Create a new session and commit
            async with tracked_db("deletion_enqueue") as new_session:
                await _do_enqueue(new_session, should_commit=True)

    except Exception as e:
        logger.exception("Failed to enqueue deletion task!")
        if settings.SENTRY.ENABLED:
            import sentry_sdk

            sentry_sdk.capture_exception(e)
        raise

>sed -n '1,360p' src/crud/representation.py

Chunk ID: 1114b6
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 3279
Output:
from __future__ import annotations

import datetime
import logging
import time
from contextlib import suppress
from typing import Any

from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession

from src import crud, exceptions, models, schemas
from src.config import settings
from src.dependencies import tracked_db
from src.dreamer.dream_scheduler import check_and_schedule_dream
from src.embedding_client import embedding_client
from src.schemas import ResolvedConfiguration
from src.telemetry.logging import accumulate_metric
from src.utils.formatting import format_datetime_utc
from src.utils.representation import (
    DeductiveObservation,
    ExplicitObservation,
    Representation,
)

logger = logging.getLogger(__name__)


def _observation_text(obs: ExplicitObservation | DeductiveObservation) -> str:
    """Return the canonical text payload for an explicit or deductive observation."""
    return obs.conclusion if isinstance(obs, DeductiveObservation) else obs.content


def _normalized_observation(
    obs: ExplicitObservation | DeductiveObservation,
) -> ExplicitObservation | DeductiveObservation:
    """Return an observation with its persisted/embed text normalized."""
    text = _observation_text(obs).strip()
    if isinstance(obs, DeductiveObservation):
        return obs.model_copy(update={"conclusion": text})
    return obs.model_copy(update={"content": text})


class RepresentationManager:
    """Unified manager for representation and document queries."""

    def __init__(
        self,
        workspace_name: str,
        *,
        observer: str,
        observed: str,
    ) -> None:
        self.workspace_name: str = workspace_name
        self.observer: str = observer
        self.observed: str = observed

    async def save_representation(
        self,
        representation: Representation,
        message_ids: list[int],
        session_name: str,
        message_created_at: datetime.datetime,
        message_level_configuration: ResolvedConfiguration,
    ) -> int:
        """
        Save Representation objects to the collection as a set of documents.

        Args:
            representation: Representation object
            message_ids: Message ID range to link with observations
            session_name: Session name to link with existing summary context
            message_created_at: Timestamp when the message was created

        Returns:
            The number of *new documents saved*
        """

        new_documents = 0

        if not representation.deductive and not representation.explicit:
            logger.debug("No observations to save")
            return new_documents

        all_observations = [
            _normalized_observation(obs)
            for obs in representation.deductive + representation.explicit
            if _observation_text(obs).strip()
        ]
        if not all_observations:
            logger.debug("No non-empty observations to save")
            return new_documents

        # Batch embed all observations
        batch_embed_start = time.perf_counter()

        observation_texts = [_observation_text(obs) for obs in all_observations]
        try:
            embeddings = await embedding_client.simple_batch_embed(observation_texts)
        except ValueError as e:
            raise exceptions.ValidationException(
                "Observation content exceeds maximum token limit of "
                + f"{settings.EMBEDDING.MAX_INPUT_TOKENS}."
            ) from e

        batch_embed_duration = (time.perf_counter() - batch_embed_start) * 1000
        accumulate_metric(
            f"deriver_{message_ids[-1]}_{self.observer}",
            "embed_new_observations",
            batch_embed_duration,
            "ms",
        )

        # Batch create document objects
        create_document_start = time.perf_counter()
        async with tracked_db("representation_manager.save_representation") as db:
            new_documents = await self._save_representation_internal(
                db,
                all_observations,
                embeddings,
                message_ids,
                session_name,
                message_created_at,
                message_level_configuration,
            )

        create_document_duration = (time.perf_counter() - create_document_start) * 1000
        accumulate_metric(
            f"deriver_{message_ids[-1]}_{self.observer}",
            "save_new_observations",
            create_document_duration,
            "ms",
        )

        return new_documents

    async def _save_representation_internal(
        self,
        db: AsyncSession,
        all_observations: list[ExplicitObservation | DeductiveObservation],
        embeddings: list[list[float]],
        message_ids: list[int],
        session_name: str,
        message_created_at: datetime.datetime,
        message_level_configuration: ResolvedConfiguration,
    ) -> int:
        # get_or_create_collection already handles IntegrityError with rollback and a retry
        collection = await crud.get_or_create_collection(
            db,
            self.workspace_name,
            observer=self.observer,
            observed=self.observed,
        )

        # Prepare all documents for bulk creation
        documents_to_create: list[schemas.DocumentCreate] = []
        for obs, embedding in zip(all_observations, embeddings, strict=True):
            # NOTE: will add additional levels of reasoning in the future
            if isinstance(obs, DeductiveObservation):
                obs_level = "deductive"
                obs_content = obs.conclusion
                obs_premises = obs.premises
            else:
                obs_level = "explicit"
                obs_content = obs.content
                obs_premises = None

            metadata: schemas.DocumentMetadata = schemas.DocumentMetadata(
                message_ids=message_ids,
                premises=obs_premises,
                message_created_at=format_datetime_utc(message_created_at),
            )

            documents_to_create.append(
                schemas.DocumentCreate(
                    content=obs_content,
                    session_name=session_name,
                    level=obs_level,
                    metadata=metadata,
                    embedding=embedding,
                )
            )

        # Use bulk creation with optional duplicate detection
        accepted_documents = await crud.create_documents(
            db,
            documents_to_create,
            self.workspace_name,
            observer=self.observer,
            observed=self.observed,
            deduplicate=settings.DERIVER.DEDUPLICATE,
        )

        if message_level_configuration.dream.enabled:
            try:
                await check_and_schedule_dream(db, collection)
            except Exception as e:
                logger.warning(f"Failed to check dream scheduling: {e}")

        return len(accepted_documents)

    async def get_working_representation(
        self,
        *,
        db: AsyncSession | None = None,
        session_name: str | None = None,
        include_semantic_query: str | None = None,
        embedding: list[float] | None = None,
        semantic_search_top_k: int | None = None,
        semantic_search_max_distance: float | None = None,
        include_most_derived: bool = False,
        max_observations: int = settings.DERIVER.WORKING_REPRESENTATION_MAX_OBSERVATIONS,
    ) -> Representation:
        """
        Get working representation with flexible query options.

        Args:
            db: Optional database session. If provided, uses it directly;
                otherwise creates a new session via tracked_db.
            session_name: Optional session to filter by
            include_semantic_query: Query for semantic search
            embedding: Pre-computed embedding for the semantic query.
            semantic_search_top_k: Number of semantic results
            semantic_search_max_distance: Maximum distance for semantic search
            include_most_derived: Include most derived observations
            max_observations: Maximum total observations to return

        Returns:
            Representation combining various query strategies
        """
        if include_semantic_query and embedding is None:
            with suppress(Exception):
                # Best-effort precompute
                embedding = await embedding_client.embed(include_semantic_query)

        if db is not None:
            return await self._get_working_representation_internal(
                db,
                session_name=session_name,
                include_semantic_query=include_semantic_query,
                embedding=embedding,
                semantic_search_top_k=semantic_search_top_k,
                semantic_search_max_distance=semantic_search_max_distance,
                include_most_derived=include_most_derived,
                max_observations=max_observations,
            )

        async with tracked_db(
            "representation_manager.get_working_representation"
        ) as new_db:
            return await self._get_working_representation_internal(
                new_db,
                session_name=session_name,
                include_semantic_query=include_semantic_query,
                embedding=embedding,
                semantic_search_top_k=semantic_search_top_k,
                semantic_search_max_distance=semantic_search_max_distance,
                include_most_derived=include_most_derived,
                max_observations=max_observations,
            )

    # Private helper methods

    async def _get_working_representation_internal(
        self,
        db: AsyncSession,
        *,
        session_name: str | None = None,
        include_semantic_query: str | None = None,
        embedding: list[float] | None = None,
        semantic_search_top_k: int | None = None,
        semantic_search_max_distance: float | None = None,
        include_most_derived: bool = False,
        max_observations: int = settings.DERIVER.WORKING_REPRESENTATION_MAX_OBSERVATIONS,
    ) -> Representation:
        """Internal implementation of get_working_representation."""
        total = max_observations

        # Calculate how many observations to get from each source
        semantic_observations = (
            min(
                max(
                    0,
                    semantic_search_top_k
                    if semantic_search_top_k is not None
                    else total // 3,
                ),
                total,
            )
            if include_semantic_query
            else 0
        )

        if include_semantic_query and include_most_derived:
            # three-way blend: both semantic and derived requested
            top_observations = min(max(0, total // 3), total - semantic_observations)
        elif include_most_derived:
            # two-way blend: only derived requested
            top_observations = min(max(0, total // 2), total - semantic_observations)
        else:
            # no derived observations requested
            top_observations = 0

        # remaining observations are recent
        recent_observations = total - semantic_observations - top_observations

        representation = Representation()

        # Get semantic observations if requested
        if include_semantic_query:
            semantic_docs = await self._query_documents_semantic(
                db,
                query=include_semantic_query,
                top_k=semantic_observations,
                max_distance=semantic_search_max_distance,
                embedding=embedding,
            )
            representation.merge_representation(
                Representation.from_documents(semantic_docs)
            )

        # Get most derived observations if requested
        if include_most_derived:
            derived_docs = await self._query_documents_most_derived(
                db, top_k=top_observations
            )
            representation.merge_representation(
                Representation.from_documents(derived_docs)
            )

        # Get recent observations
        recent_docs = await self._query_documents_recent(
            db, top_k=recent_observations, session_name=session_name
        )

        representation.merge_representation(Representation.from_documents(recent_docs))

        return representation

    async def _query_documents_semantic(
        self,
        db: AsyncSession,
        query: str,
        top_k: int,
        max_distance: float | None = None,
        level: str | None = None,
        embedding: list[float] | None = None,
    ) -> list[models.Document]:
        """Query documents by semantic similarity."""
        try:
            if level:
                return await self._query_documents_for_level(
                    db,
                    query,
                    level,
                    top_k,
                    max_distance,
                    embedding=embedding,
                )
            else:

>sed -n '1,300p' src/utils/summarizer.py

Chunk ID: cab345
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2571
Output:
import asyncio
import logging
import time
from enum import Enum
from functools import cache
from inspect import cleandoc as c
from typing import TypedDict

from sqlalchemy import update
from sqlalchemy.ext.asyncio import AsyncSession

from src import schemas
from src.cache.client import cache as cache_client
from src.config import ConfiguredModelSettings, settings
from src.crud.session import session_cache_key
from src.dependencies import tracked_db
from src.exceptions import ResourceNotFoundException
from src.llm import HonchoLLMCallResponse, honcho_llm_call
from src.models import Message
from src.telemetry import prometheus_metrics
from src.telemetry.events import AgentToolSummaryCreatedEvent, emit
from src.telemetry.logging import accumulate_metric, conditional_observe
from src.telemetry.prometheus.metrics import (
    DeriverComponents,
    DeriverTaskTypes,
    TokenTypes,
)
from src.utils.formatting import utc_now_iso
from src.utils.tokens import estimate_tokens, track_deriver_input_tokens

from .. import crud, models

logger = logging.getLogger(__name__)


# TypedDict definitions for summary data
class Summary(TypedDict):
    """
    A summary object. Stored in session metadata and used in a session's get_context.

    Attributes:
        content: The summary text.
        message_id: The primary key ID of the message that this summary covers up to.
        summary_type: The type of summary (short or long).
        created_at: The timestamp of when the summary was created (ISO format string).
        token_count: The number of tokens in the summary text.
    """

    content: str
    message_id: int
    summary_type: str
    created_at: str
    token_count: int
    message_public_id: str


def to_schema_summary(s: Summary) -> schemas.Summary:
    return schemas.Summary(
        content=s["content"],
        message_id=s["message_id"],
        summary_type=s["summary_type"],
        created_at=s["created_at"],
        token_count=[REDACTED]
        message_public_id=s.get("message_public_id", ""),
    )


# Export the public functions
__all__ = [
    "get_summary",
    "get_both_summaries",
    "get_summarized_history",
    "get_session_context",
    "get_session_context_formatted",
    "SummaryType",
    "Summary",
    "to_schema_summary",
]


def _get_summary_model_config() -> ConfiguredModelSettings:
    return settings.SUMMARY.MODEL_CONFIG


# Configuration constants for summaries
MESSAGES_PER_SHORT_SUMMARY = settings.SUMMARY.MESSAGES_PER_SHORT_SUMMARY
MESSAGES_PER_LONG_SUMMARY = settings.SUMMARY.MESSAGES_PER_LONG_SUMMARY

SUMMARIES_KEY = "summaries"


# The types of summary to store in the session metadata
class SummaryType(Enum):
    SHORT = "honcho_chat_summary_short"
    LONG = "honcho_chat_summary_long"


def short_summary_prompt(
    formatted_messages: str,
    output_words: int,
    previous_summary_text: str,
) -> str:
    """Generate the short summary prompt."""
    return c(f"""
You are a system that summarizes parts of a conversation to create a concise and accurate summary. Focus on capturing:

1. Key facts and information shared (**Capture as many explicit facts as possible**)
2. User preferences, opinions, and questions
3. Important context and requests
4. Core topics discussed

If there is a previous summary, ALWAYS make your new summary inclusive of both it and the new messages, therefore capturing the ENTIRE conversation. Prioritize key facts across the entire conversation.

Provide a concise, factual summary that captures the essence of the conversation. Your summary should be detailed enough to serve as context for future messages, but brief enough to be helpful. Prefer a thorough chronological narrative over a list of bullet points.

Return only the summary without any explanation or meta-commentary.

<previous_summary>
{previous_summary_text}
</previous_summary>

<conversation>
{formatted_messages}
</conversation>

Hard limit: {output_words} words maximum. If needed, drop lower-priority detail to stay within the limit.
""")


def long_summary_prompt(
    formatted_messages: str,
    output_words: int,
    previous_summary_text: str,
) -> str:
    """Generate the long summary prompt."""
    return c(f"""
You are a system that creates thorough, comprehensive summaries of conversations. Focus on capturing:

1. Key facts and information shared (**Capture as many explicit facts as possible**)
2. User preferences, opinions, and questions
3. Important context and requests
4. Core topics discussed in detail
5. User's apparent emotional state and personality traits
6. Important themes and patterns across the conversation

If there is a previous summary, ALWAYS make your new summary inclusive of both it and the new messages, therefore capturing the ENTIRE conversation. Prioritize key facts across the entire conversation.

Provide a thorough and detailed summary that captures the essence of the conversation. Your summary should serve as a comprehensive record of the important information in this conversation. Prefer an exhaustive chronological narrative over a list of bullet points.

Return only the summary without any explanation or meta-commentary.

<previous_summary>
{previous_summary_text}
</previous_summary>

<conversation>
{formatted_messages}
</conversation>

Hard limit: {output_words} words maximum. If needed, drop lower-priority detail to stay within the limit.
""")


@cache
def estimate_short_summary_prompt_tokens() -> int:
    """Estimate tokens for the short summary prompt (without messages/previous_summary)."""
    try:
        return estimate_tokens(
            short_summary_prompt(
                formatted_messages="",
                output_words=0,
                previous_summary_text="",
            )
        )
    except Exception:
        # Return a rough estimate if estimation fails
        return 200


@cache
def estimate_long_summary_prompt_tokens() -> int:
    """Estimate tokens for the long summary prompt (without messages/previous_summary)."""
    try:
        return estimate_tokens(
            long_summary_prompt(
                formatted_messages="",
                output_words=0,
                previous_summary_text="",
            )
        )
    except Exception:
        # Return a rough estimate if estimation fails
        return 200


@conditional_observe(name="Create Short Summary")
async def create_short_summary(
    formatted_messages: str,
    input_tokens: int,
    previous_summary: str | None = None,
) -> HonchoLLMCallResponse[str]:
    # input_tokens indicates how many tokens the message list + previous summary take up
    # we want to optimize short summaries to be smaller than the actual content being summarized
    # so we ask the agent to produce a word count roughly equal to either the input, or the max
    # size if the input is larger. the word/token ratio is roughly 4:3 so we multiply by 0.75.
    # LLMs *seem* to respond better to getting asked for a word count but should workshop this.
    output_words = int(min(input_tokens, settings.SUMMARY.MAX_TOKENS_SHORT) * 0.75)

    if previous_summary:
        previous_summary_text = previous_summary
    else:
        previous_summary_text = "There is no previous summary -- the messages are the beginning of the conversation."

    prompt = short_summary_prompt(
        formatted_messages, output_words, previous_summary_text
    )

    return await honcho_llm_call(
        model_config=_get_summary_model_config(),
        prompt=prompt,
        max_tokens=[REDACTED]
    )


@conditional_observe(name="Create Long Summary")
async def create_long_summary(
    formatted_messages: str,
    previous_summary: str | None = None,
) -> HonchoLLMCallResponse[str]:
    # the word/token ratio is roughly 4:3 so we multiply by 0.75.
    # LLMs *seem* to respond better to getting asked for a word count but should workshop this.
    output_words = int(settings.SUMMARY.MAX_TOKENS_LONG * 0.75)

    if previous_summary:
        previous_summary_text = previous_summary
    else:
        previous_summary_text = "There is no previous summary -- the messages are the beginning of the conversation."

    prompt = long_summary_prompt(
        formatted_messages, output_words, previous_summary_text
    )

    return await honcho_llm_call(
        model_config=_get_summary_model_config(),
        prompt=prompt,
        max_tokens=[REDACTED]
    )


async def summarize_if_needed(
    workspace_name: str,
    session_name: str,
    message_id: int,
    message_seq_in_session: int,
    message_public_id: str,
    configuration: schemas.ResolvedConfiguration,
) -> None:
    """
    Create short/long summaries if thresholds met.

    This function checks for both short and long summary needs independently,
    without assuming any relationship between their thresholds.

    Args:
        workspace_name: The workspace name
        session_name: The session name
        message_id: The message ID
        message_seq_in_session: The sequence number of the message in the session
        message_public_id: The public ID of the message
        configuration: The resolved configuration for the message
    """
    if configuration.summary.enabled is False:
        return

    should_create_long: bool = (
        message_seq_in_session % configuration.summary.messages_per_long_summary == 0
    )
    should_create_short: bool = (
        message_seq_in_session % configuration.summary.messages_per_short_summary == 0
    )

    if should_create_long is False and should_create_short is False:
        return

    # If both summaries need to be created, run them in parallel
    if should_create_long and should_create_short:

        async def create_long_summary_task():
            await _create_and_save_summary(
                workspace_name,
                session_name,
                message_id=message_id,
                message_seq_in_session=message_seq_in_session,
                message_public_id=message_public_id,
                summary_type=SummaryType.LONG,
                configuration=configuration,
            )
            accumulate_metric(
                f"summary_{workspace_name}_{message_id}",
                "long_summary_up_to_message",

>sed -n '1,280p' src/dialectic/chat.py

Chunk ID: 37e844
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1209
Output:
"""
Chat functionality for the Dialectic API.

Provides the agentic_chat function for answering queries about peers
using the DialecticAgent.
"""

import logging
from collections.abc import AsyncIterator

from src import crud, schemas
from src.config import ReasoningLevel
from src.dependencies import tracked_db
from src.dialectic.core import DialecticAgent
from src.utils.config_helpers import get_configuration

logger = logging.getLogger(__name__)


async def agentic_chat(
    workspace_name: str,
    session_name: str | None,
    query: str,
    observer: str,
    observed: str,
    reasoning_level: ReasoningLevel = "low",
) -> str:
    """
    Answer a query about a peer using the agentic dialectic.

    Args:
        workspace_name: Workspace identifier
        session_name: Session identifier (may be None for global queries)
        query: The question to answer about the peer
        observer: The peer making the query
        observed: The peer being queried about
        reasoning_level: Level of reasoning to apply

    Returns:
        The synthesized answer string
    """
    # Short-lived DB session for validation + config
    async with tracked_db("dialectic.preflight") as db:
        await crud.get_peer(db, workspace_name, schemas.PeerCreate(name=observer))
        if observer != observed:
            await crud.get_peer(db, workspace_name, schemas.PeerCreate(name=observed))

        session = None
        if session_name:
            session = await crud.get_session(
                db, workspace_name=workspace_name, session_name=session_name
            )
        workspace = await crud.get_workspace(db, workspace_name=workspace_name)
        configuration = get_configuration(None, session, workspace)

        observer_peer_card = None
        observed_peer_card = None
        if configuration.peer_card.use:
            observer_peer_card = await crud.get_peer_card(
                db, workspace_name, observer=observer, observed=observer
            )
            if observer != observed:
                observed_peer_card = await crud.get_peer_card(
                    db, workspace_name, observer=observer, observed=observed
                )
    # DB session closed — agent runs without holding a connection

    agent = DialecticAgent(
        workspace_name=workspace_name,
        session_name=session_name,
        observer=observer,
        observed=observed,
        observer_peer_card=observer_peer_card,
        observed_peer_card=observed_peer_card,
        reasoning_level=reasoning_level,
    )

    return await agent.answer(query)


async def agentic_chat_stream(
    workspace_name: str,
    session_name: str | None,
    query: str,
    observer: str,
    observed: str,
    reasoning_level: ReasoningLevel = "low",
) -> AsyncIterator[str]:
    """
    Stream an answer to a query about a peer using the agentic dialectic.

    Args:
        workspace_name: Workspace identifier
        session_name: Session identifier (may be None for global queries)
        query: The question to answer about the peer
        observer: The peer making the query
        observed: The peer being queried about
        reasoning_level: Level of reasoning to apply

    Yields:
        Chunks of the response text as they are generated
    """
    # Short-lived DB session for validation + config
    async with tracked_db("dialectic.preflight") as db:
        await crud.get_peer(db, workspace_name, schemas.PeerCreate(name=observer))
        if observer != observed:
            await crud.get_peer(db, workspace_name, schemas.PeerCreate(name=observed))

        session = None
        if session_name:
            session = await crud.get_session(
                db, workspace_name=workspace_name, session_name=session_name
            )
        workspace = await crud.get_workspace(db, workspace_name=workspace_name)
        configuration = get_configuration(None, session, workspace)

        observer_peer_card = None
        observed_peer_card = None
        if configuration.peer_card.use:
            observer_peer_card = await crud.get_peer_card(
                db, workspace_name, observer=observer, observed=observer
            )
            if observer != observed:
                observed_peer_card = await crud.get_peer_card(
                    db, workspace_name, observer=observer, observed=observed
                )
    # DB session closed — agent streams without holding a connection

    agent = DialecticAgent(
        workspace_name=workspace_name,
        session_name=session_name,
        observer=observer,
        observed=observed,
        observer_peer_card=observer_peer_card,
        observed_peer_card=observed_peer_card,
        reasoning_level=reasoning_level,
    )

    async for chunk in agent.answer_stream(query):
        yield chunk

>sed -n '1,360p' src/dreamer/orchestrator.py

Chunk ID: 838293
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 3434
Output:
"""
Dream orchestrator for the specialist-based architecture.

This module coordinates the full dream cycle:
0. [Optional] Surprisal sampling: Pre-filter observations by geometric surprisal
1. Run deduction specialist (self-directed exploration, creates deductive observations)
2. Run induction specialist (self-directed exploration, creates inductive observations)

Specialists are self-directed agents that explore the observation space and create
higher-level observations. When surprisal sampling finds interesting observations,
they're passed as hints, but specialists are free to follow the evidence wherever it leads.
"""

from __future__ import annotations

import logging
import time
import uuid
from dataclasses import dataclass
from datetime import datetime, timezone
from typing import Any

import sentry_sdk
from sqlalchemy import func, select

from src import crud, models
from src.config import settings
from src.dependencies import tracked_db
from src.dreamer.specialists import SPECIALISTS, SpecialistResult
from src.dreamer.surprisal import SurprisalScore  # type: ignore
from src.exceptions import SpecialistExecutionError, SurprisalError
from src.schemas import DreamType
from src.telemetry.events import DreamRunEvent, emit
from src.telemetry.logging import (
    accumulate_metric,
    log_performance_metrics,
)
from src.utils.config_helpers import get_configuration
from src.utils.queue_payload import DreamPayload

logger = logging.getLogger(__name__)


@dataclass
class DreamResult:
    """Result of a dream cycle for telemetry reporting."""

    # Run identification
    run_id: str
    specialists_run: list[str]

    # Specialist outcomes
    deduction_success: bool
    induction_success: bool

    # Surprisal sampling
    surprisal_enabled: bool
    surprisal_conclusion_count: int

    # Aggregate metrics
    total_iterations: int
    total_duration_ms: float
    input_tokens: int
    output_tokens: int


async def run_dream(
    workspace_name: str,
    observer: str,
    observed: str,
    session_name: str | None = None,
) -> DreamResult | None:
    """
    Run a full dream cycle with optional surprisal-based sampling.

    The dream cycle runs specialists sequentially:
    0. [Optional] Surprisal sampling: Pre-filter observations by geometric surprisal
    1. Deduction specialist: Creates deductive observations from explicit facts
    2. Induction specialist: Creates inductive observations from patterns

    Uses short-lived DB sessions to avoid holding connections during LLM calls.

    Args:
        workspace_name: Workspace identifier
        observer: Observer peer name
        observed: Observed peer name
        session_name: Session identifier if specified
    """
    if not settings.DREAM.ENABLED:
        return None

    run_id = str(uuid.uuid4())[:8]
    task_name = f"dream_orchestrator_{run_id}"
    start_time = time.perf_counter()

    logger.info(
        f"[{run_id}] Starting dream cycle for {workspace_name}/{observer}/{observed}"
    )

    # Short-lived DB session for config resolution
    async with tracked_db("dream.config") as db:
        if session_name is not None:
            session = await crud.get_session(
                db, workspace_name=workspace_name, session_name=session_name
            )
        else:
            session = None

        workspace = await crud.get_workspace(db, workspace_name=workspace_name)
        configuration = get_configuration(None, session, workspace)
    if not configuration.dream.enabled:
        logger.info(
            f"[{run_id}] Dreams disabled for {workspace_name}/{session_name}, skipping dream"
        )
        return None

    # Track specialist outcomes
    deduction_success = False
    induction_success = False
    surprisal_observation_count = 0
    deduction_result: SpecialistResult | None = None
    induction_result: SpecialistResult | None = None

    # Phase 0: Surprisal-based sampling (if enabled)
    # Specialists are self-directed by default - hints are optional suggestions
    exploration_hints: list[str] | None = None

    if settings.DREAM.SURPRISAL.ENABLED:
        logger.info(f"[{run_id}] Phase 0: Computing surprisal scores")
        try:
            from src.dreamer.surprisal import sample_observations_with_surprisal

            high_surprisal_obs = await sample_observations_with_surprisal(
                workspace_name=workspace_name,
                observer=observer,
                observed=observed,
            )

            logger.info(
                f"[{run_id}] Surprisal: Found {len(high_surprisal_obs)} high-surprisal observations"
            )
            surprisal_observation_count = len(high_surprisal_obs)
            accumulate_metric(
                task_name, "surprisal_observations", len(high_surprisal_obs), "count"
            )

            if len(high_surprisal_obs) > 0:
                # Use high-surprisal observations as hints for exploration
                exploration_hints = _create_queries_from_surprisal(high_surprisal_obs)
                logger.info(
                    f"[{run_id}] ✨ SURPRISAL HINTS: Suggesting {len(exploration_hints)} "
                    + "high-surprisal topics for specialists to investigate"
                )
                logger.info(
                    f"[{run_id}] Targeting observations with surprisal range: "
                    + f"{high_surprisal_obs[-1].surprisal:.3f} to {high_surprisal_obs[0].surprisal:.3f}"
                )
            else:
                logger.info(
                    f"[{run_id}] No high-surprisal observations - specialists will explore freely"
                )

        except SurprisalError as e:
            logger.error(f"[{run_id}] Surprisal sampling failed: {e}", exc_info=True)
            accumulate_metric(task_name, "surprisal_error", str(e), "blob")
            # Specialists will explore freely without hints

    # Phase 1: Run deduction specialist (manages its own DB sessions)
    logger.info(f"[{run_id}] Phase 1: Running deduction specialist")
    deduction_specialist = SPECIALISTS["deduction"]
    try:
        deduction_result = await deduction_specialist.run(
            workspace_name=workspace_name,
            observer=observer,
            observed=observed,
            session_name=session_name,
            hints=exploration_hints,
            configuration=configuration,
            parent_run_id=run_id,
        )
        logger.info(
            f"[{run_id}] Deduction completed: {deduction_result.content[:200]}..."
        )
        accumulate_metric(
            task_name, "deduction_result", deduction_result.content, "blob"
        )
        deduction_success = deduction_result.success
    except SpecialistExecutionError as e:
        logger.error(f"[{run_id}] Deduction specialist failed: {e}", exc_info=True)
        accumulate_metric(task_name, "deduction_error", str(e), "blob")

    # Phase 2: Run induction specialist (after deduction so it can see new deductive obs)
    logger.info(f"[{run_id}] Phase 2: Running induction specialist")
    induction_specialist = SPECIALISTS["induction"]
    try:
        induction_result = await induction_specialist.run(
            workspace_name=workspace_name,
            observer=observer,
            observed=observed,
            session_name=session_name,
            hints=exploration_hints,
            configuration=configuration,
            parent_run_id=run_id,
        )
        logger.info(
            f"[{run_id}] Induction completed: {induction_result.content[:200]}..."
        )
        accumulate_metric(
            task_name, "induction_result", induction_result.content, "blob"
        )
        induction_success = induction_result.success
    except SpecialistExecutionError as e:
        logger.error(f"[{run_id}] Induction specialist failed: {e}", exc_info=True)
        accumulate_metric(task_name, "induction_error", str(e), "blob")

    # Log final metrics
    duration_ms = (time.perf_counter() - start_time) * 1000
    accumulate_metric(task_name, "total_duration", duration_ms, "ms")

    logger.info(f"[{run_id}] Dream cycle completed in {duration_ms:.0f}ms")
    log_performance_metrics("dream_orchestrator", run_id)

    # Aggregate metrics from specialist results
    total_iterations = (deduction_result.iterations if deduction_result else 0) + (
        induction_result.iterations if induction_result else 0
    )
    total_input_tokens = (deduction_result.input_tokens if deduction_result else 0) + (
        induction_result.input_tokens if induction_result else 0
    )
    total_output_tokens = (
        deduction_result.output_tokens if deduction_result else 0
    ) + (induction_result.output_tokens if induction_result else 0)

    # Emit DreamRunEvent with aggregated metrics
    emit(
        DreamRunEvent(
            run_id=run_id,
            workspace_name=workspace_name,
            session_name=session_name,
            observer=observer,
            observed=observed,
            specialists_run=["deduction", "induction"],
            deduction_success=deduction_success,
            induction_success=induction_success,
            surprisal_enabled=settings.DREAM.SURPRISAL.ENABLED,
            surprisal_conclusion_count=surprisal_observation_count,
            total_iterations=total_iterations,
            total_input_tokens=[REDACTED]
            total_output_tokens=[REDACTED]
            total_duration_ms=duration_ms,
        )
    )

    return DreamResult(
        run_id=run_id,
        specialists_run=["deduction", "induction"],
        deduction_success=deduction_success,
        induction_success=induction_success,
        surprisal_enabled=settings.DREAM.SURPRISAL.ENABLED,
        surprisal_conclusion_count=surprisal_observation_count,
        total_iterations=total_iterations,
        total_duration_ms=duration_ms,
        input_tokens=[REDACTED]
        output_tokens=[REDACTED]
    )


def _create_queries_from_surprisal(
    high_surprisal_obs: list[SurprisalScore],
) -> list[str]:
    """
    Create search queries from high-surprisal observations.

    Strategy: Use observation content as semantic search queries.
    Truncate if too long (>200 chars).

    Args:
        high_surprisal_obs: List of SurprisalScore objects

    Returns:
        List of query strings (max 10)
    """
    queries: list[Any] = []
    for score in high_surprisal_obs:
        content = score.observation.content
        if len(content) > 200:
            content = content[:200] + "..."
        queries.append(content)
    return queries[:10]  # Limit to 10 queries


@sentry_sdk.trace
async def process_dream(
    payload: DreamPayload,
    workspace_name: str,
) -> None:
    """
    Process a dream task by performing collection maintenance operations.

    Args:
        payload: The dream task payload containing workspace, peer, and dream type information
    """
    logger.info(
        f"""
(っ- ‸ - ς)ᶻ z 𐰁 ᶻ z 𐰁 ᶻ z 𐰁\n
DREAM: {payload.dream_type} documents for {workspace_name}/{payload.observer}/{payload.observed}\n
𐰁 z ᶻ 𐰁 z ᶻ 𐰁 z ᶻ(っ- ‸ - ς)"""
    )

    try:
        match payload.dream_type:
            case DreamType.OMNI:
                result = await run_dream(
                    workspace_name=workspace_name,
                    observer=payload.observer,
                    observed=payload.observed,
                    session_name=payload.session_name,
                )

                # Log completion (telemetry event already emitted in run_dream)
                if result is not None:
                    logger.info(
                        f"Dream completed: run_id={result.run_id}, "
                        + f"iterations={result.total_iterations}, "
                        + f"duration={result.total_duration_ms:.0f}ms"
                    )

                    # Both guard fields advance together only on successful consolidation.
                    now_iso = datetime.now(timezone.utc).isoformat()
                    async with tracked_db("dream.guard_pair_write") as db:
                        collection = await crud.get_collection(
                            db,
                            workspace_name,
                            observer=payload.observer,
                            observed=payload.observed,
                            with_for_update=True,
                        )
                        count_stmt = select(func.count(models.Document.id)).where(
                            models.Document.workspace_name == workspace_name,
                            models.Document.observer == payload.observer,
                            models.Document.observed == payload.observed,
                            models.Document.level == "explicit",
                        )
                        current_explicit_count = int(await db.scalar(count_stmt) or 0)
                        dream_meta = dict(collection.internal_metadata.get("dream", {}))
                        dream_meta["last_dream_at"] = now_iso
                        dream_meta["last_dream_document_count"] = current_explicit_count
                        await crud.update_collection_internal_metadata(
                            db,
                            workspace_name,
                            payload.observer,
                            payload.observed,
                            update_data={"dream": dream_meta},
                        )

    except Exception as e:
        logger.error(
            f"Error processing dream task {payload.dream_type} for {payload.observer}/{payload.observed}: {str(e)}",
            exc_info=True,
        )

>sed -n '360,820p' src/crud/representation.py

Chunk ID: e19314
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1373
Output:
            else:
                documents = await crud.query_documents(
                    db,
                    workspace_name=self.workspace_name,
                    observer=self.observer,
                    observed=self.observed,
                    query=query,
                    max_distance=max_distance,
                    top_k=top_k,
                    embedding=embedding,
                )
                db.expunge_all()
                return list(documents)

        except Exception as e:
            logger.error(f"Error getting relevant observations: {e}")
            return []

    async def _query_documents_recent(
        self, db: AsyncSession, top_k: int, session_name: str | None = None
    ) -> list[models.Document]:
        """Query most recent documents."""
        stmt = (
            select(models.Document)
            .limit(top_k)
            .where(
                models.Document.workspace_name == self.workspace_name,
                models.Document.observer == self.observer,
                models.Document.observed == self.observed,
                models.Document.deleted_at.is_(None),
                *(
                    [models.Document.session_name == session_name]
                    if session_name is not None
                    else []
                ),
            )
            .order_by(models.Document.created_at.desc())
        )

        result = await db.execute(stmt)
        documents = result.scalars().all()
        db.expunge_all()
        return list(documents)

    async def _query_documents_most_derived(
        self, db: AsyncSession, top_k: int
    ) -> list[models.Document]:
        """Query most derived documents."""
        stmt = (
            select(models.Document)
            .limit(top_k)
            .where(
                models.Document.workspace_name == self.workspace_name,
                models.Document.observer == self.observer,
                models.Document.observed == self.observed,
                models.Document.deleted_at.is_(None),
            )
            .order_by(models.Document.times_derived.desc())
        )

        result = await db.execute(stmt)
        documents = result.scalars().all()
        db.expunge_all()
        return list(documents)

    async def _get_observations_internal(
        self,
        db: AsyncSession,
        query: str,
        top_k: int,
        max_distance: float,
        level: str | None,
    ) -> list[models.Document]:
        """Internal method that does the actual observation retrieval."""
        return await self._query_documents_semantic(
            db, query, top_k, max_distance, level
        )

    async def _query_documents_for_level(
        self,
        db: AsyncSession,
        query: str,
        level: str,
        count: int,
        max_distance: float | None = None,
        embedding: list[float] | None = None,
    ) -> list[models.Document]:
        """Query documents for a specific level."""
        documents = await crud.query_documents(
            db,
            workspace_name=self.workspace_name,
            observer=self.observer,
            observed=self.observed,
            query=query,
            max_distance=max_distance,
            top_k=count,
            filters=self._build_filter_conditions(level),
            embedding=embedding,
        )

        # Sort by creation time
        docs_sorted: list[models.Document] = sorted(
            list(documents), key=lambda x: x.created_at, reverse=True
        )
        return docs_sorted

    def _build_filter_conditions(
        self,
        level: str | None = None,
    ) -> dict[str, Any]:
        """
        Build filter conditions for document queries.

        Returns a flat dict of key-value pairs for vector store filtering.
        """
        filters: dict[str, Any] = {}

        if level:
            filters["level"] = level

        return filters


# Module-level functions for backward compatibility and convenience


async def get_working_representation(
    workspace_name: str,
    *,
    db: AsyncSession | None = None,
    observer: str,
    observed: str,
    session_name: str | None = None,
    include_semantic_query: str | None = None,
    embedding: list[float] | None = None,
    semantic_search_top_k: int | None = None,
    semantic_search_max_distance: float | None = None,
    include_most_derived: bool = False,
    max_observations: int = settings.DERIVER.WORKING_REPRESENTATION_MAX_OBSERVATIONS,
) -> Representation:
    """
    Get raw working representation data from the relevant document collection.

    This is a convenience function that creates a RepresentationManager and calls
    get_working_representation on it.

    Args:
        db: Optional database session. If provided, uses it directly;
            otherwise creates a new session via tracked_db.
        embedding: Pre-computed embedding for the semantic query.
    """
    manager = RepresentationManager(
        workspace_name=workspace_name,
        observer=observer,
        observed=observed,
    )
    return await manager.get_working_representation(
        db=db,
        session_name=session_name,
        include_semantic_query=include_semantic_query,
        embedding=embedding,
        semantic_search_top_k=semantic_search_top_k,
        semantic_search_max_distance=semantic_search_max_distance,
        include_most_derived=include_most_derived,
        max_observations=max_observations,
    )

>sed -n '300,700p' src/utils/summarizer.py

Chunk ID: 7cbf40
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 3516
Output:
                "long_summary_up_to_message",
                message_seq_in_session,
                "count",
            )

        async def create_short_summary_task():
            await _create_and_save_summary(
                workspace_name,
                session_name,
                message_id=message_id,
                message_seq_in_session=message_seq_in_session,
                message_public_id=message_public_id,
                summary_type=SummaryType.SHORT,
                configuration=configuration,
            )
            accumulate_metric(
                f"summary_{workspace_name}_{message_id}",
                "short_summary_up_to_message",
                message_seq_in_session,
                "count",
            )

        await asyncio.gather(
            create_long_summary_task(),
            create_short_summary_task(),
            return_exceptions=True,
        )
    else:
        # If only one summary needs to be created, run individually
        if should_create_long:
            await _create_and_save_summary(
                workspace_name,
                session_name,
                message_id=message_id,
                message_seq_in_session=message_seq_in_session,
                message_public_id=message_public_id,
                summary_type=SummaryType.LONG,
                configuration=configuration,
            )
            accumulate_metric(
                f"summary_{workspace_name}_{message_id}",
                "long_summary_up_to_message",
                message_seq_in_session,
                "count",
            )
        elif should_create_short:
            await _create_and_save_summary(
                workspace_name,
                session_name,
                message_id=message_id,
                message_seq_in_session=message_seq_in_session,
                message_public_id=message_public_id,
                summary_type=SummaryType.SHORT,
                configuration=configuration,
            )
            accumulate_metric(
                f"summary_{workspace_name}_{message_id}",
                "short_summary_up_to_message",
                message_seq_in_session,
                "count",
            )


async def _create_and_save_summary(
    workspace_name: str,
    session_name: str,
    *,
    message_id: int,
    message_seq_in_session: int,
    message_public_id: str,
    summary_type: SummaryType,
    configuration: schemas.ResolvedConfiguration,
) -> None:
    """
    Create a new summary and save it to the database.
    1. Get the latest summary
    2. Get the messages since the latest summary
    3. Generate a new summary using the messages and the previous summary
    4. Save the new summary to the database
    """

    logger.debug("Creating new %s summary", summary_type.name)
    summary_start = time.perf_counter()

    async with tracked_db("summary.fetch_data") as db:
        latest_summary = await get_summary(
            db, workspace_name, session_name, summary_type
        )
        if latest_summary:
            latest_summary_message_id = latest_summary["message_id"]
            # Skip if latest summary already covers message.
            if latest_summary_message_id >= message_id:
                return

        previous_summary_text = latest_summary["content"] if latest_summary else None

        # Calculate the sequence range for messages to summarize
        # We want to get the last N messages where N is the configured summary interval
        messages_per_summary = (
            configuration.summary.messages_per_long_summary
            if summary_type == SummaryType.LONG
            else configuration.summary.messages_per_short_summary
        )
        start_seq = max(message_seq_in_session - messages_per_summary + 1, 1)

        messages: list[Message] = await crud.get_messages_by_seq_range(
            db,
            workspace_name,
            session_name,
            start_seq=start_seq,
            end_seq=message_seq_in_session,
        )
        if not messages:
            logger.warning("No messages to summarize for message %s", message_id)
            return

        # Extract values before closing session
        formatted_messages = _format_messages(messages)
        last_message_id = messages[-1].id
        last_message_content_preview = messages[-1].content[:30]
        message_count = len(messages)

        messages_tokens = sum([message.token_count for message in messages])
        previous_summary_tokens = latest_summary["token_count"] if latest_summary else 0
        input_tokens = messages_tokens + previous_summary_tokens

    (
        new_summary,
        is_fallback,
        llm_input_tokens,
        llm_output_tokens,
    ) = await _create_summary(
        formatted_messages=formatted_messages,
        previous_summary_text=previous_summary_text,
        summary_type=summary_type,
        input_tokens=[REDACTED]
        message_public_id=message_public_id,
        last_message_id=last_message_id,
        last_message_content_preview=last_message_content_preview,
        message_count=message_count,
    )

    # Step 3: Save to database with new transaction
    if not is_fallback:
        # Get base prompt tokens based on summary type
        if summary_type == SummaryType.SHORT:
            prompt_tokens = estimate_short_summary_prompt_tokens()
        else:
            prompt_tokens = estimate_long_summary_prompt_tokens()

        track_deriver_input_tokens(
            task_type=DeriverTaskTypes.SUMMARY,
            components={
                DeriverComponents.PROMPT: prompt_tokens,
                DeriverComponents.MESSAGES: messages_tokens,
                DeriverComponents.PREVIOUS_SUMMARY: previous_summary_tokens,
            },
        )

        # Track output tokens
        if settings.METRICS.ENABLED:
            prometheus_metrics.record_deriver_tokens(
                count=new_summary["token_count"],
                task_type=DeriverTaskTypes.SUMMARY.value,
                token_type=[REDACTED]
                component=DeriverComponents.OUTPUT_TOTAL.value,
            )

        # Save summary to database with new transaction
        async with tracked_db("summary.save") as db:
            await _save_summary(
                db,
                new_summary,
                workspace_name,
                session_name,
            )

    accumulate_metric(
        f"summary_{workspace_name}_{message_id}",
        f"{summary_type.name}_summary_text",
        new_summary["content"],
        "blob",
    )
    accumulate_metric(
        f"summary_{workspace_name}_{message_id}",
        f"{summary_type.name}_summary_size",
        new_summary["token_count"],
        "tokens",
    )

    summary_duration = (time.perf_counter() - summary_start) * 1000
    accumulate_metric(
        f"summary_{workspace_name}_{message_id}",
        f"{summary_type.name}_summary_creation",
        summary_duration,
        "ms",
    )

    # Emit telemetry event (only for non-fallback summaries)
    # Note: Using AgentToolSummaryCreatedEvent with dummy run_id/iteration since
    # this is called from the deriver, not from an agentic loop
    if not is_fallback:
        emit(
            AgentToolSummaryCreatedEvent(
                run_id="deriver",  # Placeholder - not from an agentic run
                iteration=0,  # Placeholder - not from an agentic loop
                parent_category="deriver",
                agent_type="summarizer",
                workspace_name=workspace_name,
                session_name=session_name,
                message_id=message_public_id,
                message_count=len(messages),
                message_seq_in_session=message_seq_in_session,
                summary_type="short" if summary_type == SummaryType.SHORT else "long",
                input_tokens=[REDACTED]
                output_tokens=[REDACTED]
            )
        )


async def _create_summary(
    formatted_messages: str,
    previous_summary_text: str | None,
    summary_type: SummaryType,
    input_tokens: int,
    message_public_id: str,
    last_message_id: int,
    last_message_content_preview: str,
    message_count: int,
) -> tuple[Summary, bool, int, int]:
    """
    Generate a summary of the provided messages using an LLM.

    Args:
        formatted_messages: Pre-formatted message string
        previous_summary_text: Optional previous summary to provide context
        summary_type: Type of summary to create ("short" or "long")
        input_tokens: Token count for input
        message_public_id: Public ID of the last message
        last_message_id: ID of the last message
        last_message_content_preview: Preview of last message content for fallback
        message_count: Number of messages for fallback

    Returns:
        A tuple of (Summary, is_fallback, llm_input_tokens, llm_output_tokens)
        where is_fallback indicates if the summary was generated using a
        fallback instead of an LLM call, and the token counts are from the LLM call
        (0 if fallback was used)
    """

    response: HonchoLLMCallResponse[str] | None = None
    is_fallback = False
    llm_input_tokens = 0
    llm_output_tokens = 0
    try:
        if summary_type == SummaryType.SHORT:
            response = await create_short_summary(
                formatted_messages, input_tokens, previous_summary_text
            )
        else:
            response = await create_long_summary(
                formatted_messages, previous_summary_text
            )

        summary_text = response.content
        summary_tokens = response.output_tokens
        llm_input_tokens = response.input_tokens
        llm_output_tokens = response.output_tokens

        # Detect potential issues with the summary
        if not summary_text.strip():
            logger.error(
                "Generated summary is empty (finish_reasons=%s). Falling back to basic summary.",
                response.finish_reasons,
            )
            is_fallback = True
            summary_text = (
                f"Conversation with {message_count} messages about {last_message_content_preview}..."
                if message_count > 0
                else ""
            )
            summary_tokens = estimate_tokens(summary_text) if summary_text else 0
            llm_input_tokens = 0
            llm_output_tokens = 0
    except Exception:
        logger.exception("Error generating summary!")
        # Fallback to a basic summary in case of error
        summary_text = (
            f"Conversation with {message_count} messages about {last_message_content_preview}..."
            if message_count > 0
            else ""
        )
        summary_tokens = 0
        is_fallback = True

    return (
        Summary(
            content=summary_text,
            message_id=last_message_id,
            summary_type=summary_type.value,
            created_at=utc_now_iso(),
            token_count=[REDACTED]
            message_public_id=message_public_id,
        ),
        is_fallback,
        llm_input_tokens,
        llm_output_tokens,
    )


async def _save_summary(
    db: AsyncSession,
    summary: Summary,
    workspace_name: str,
    session_name: str,
) -> None:
    """
    Save a summary as metadata on a session.

    Args:
        db: Database session
        summary: The summary to save
        workspace_name: Workspace name
        session_name: Session name
    """
    from src.exceptions import ResourceNotFoundException

    # Get the label value from the enum
    label_value = summary["summary_type"]

    try:
        session = await crud.get_session(db, session_name, workspace_name)
    except ResourceNotFoundException:
        # If session doesn't exist, we can't save the summary
        logger.warning(
            f"Cannot save summary: session {session_name} not found in workspace {workspace_name}"
        )
        return

    # Use SQLAlchemy update() with PostgreSQL's || operator to properly merge JSONB
    # We need to merge the new summary into the existing summaries structure
    update_data = {}
    existing_summaries = session.internal_metadata.get(SUMMARIES_KEY, {})
    existing_summaries[label_value] = summary
    update_data[SUMMARIES_KEY] = existing_summaries

    stmt = (
        update(models.Session)
        .where(models.Session.workspace_name == workspace_name)
        .where(models.Session.name == session_name)
        .values(
            internal_metadata=models.Session.internal_metadata.op("||")(update_data)
        )
    )

    await db.execute(stmt)
    await db.commit()

    cache_key = session_cache_key(workspace_name, session_name)
    await cache_client.delete(cache_key)


async def get_summarized_history(
    db: AsyncSession,
    workspace_name: str,
    session_name: str,
    cutoff: int | None = None,
    summary_type: SummaryType = SummaryType.SHORT,
) -> str:
    """
    Get a summarized version of the chat history by combining the latest summary
    with all messages since that summary.

    Note: history is exclusive of the cutoff message.

    Args:
        db: Database session
        workspace_name: The workspace name
        session_name: The session name
        cutoff: (Optional) message ID to cutoff at
        summary_type: Type of summary to get ("short" or "long")

    Returns:
        A string formatted history text with summary and recent messages
    """
    # Get messages since the latest summary and the summary itself
    summary = await get_summary(db, workspace_name, session_name, summary_type)

    # Check if we have a valid summary with a message_id
    if summary:
        messages = await crud.get_messages_id_range(
            db,
            workspace_name,
            session_name,
            start_id=summary["message_id"],
            end_id=cutoff,
        )
    else:
        messages = await crud.get_messages_id_range(
            db, workspace_name, session_name, end_id=cutoff
        )

>sed -n '1,360p' src/dialectic/core.py

Chunk ID: 374989
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 3361
Output:
"""
Core Dialectic Agent implementation.

This agent uses tools to gather context from the memory system
and synthesize responses to queries about a peer.
"""

import logging
import time
import uuid
from collections.abc import AsyncIterator, Callable
from typing import Any, cast

from src import crud
from src.config import ConfiguredModelSettings, ReasoningLevel, settings
from src.dependencies import tracked_db
from src.dialectic import prompts
from src.embedding_client import embedding_client
from src.llm import (
    HonchoLLMCallResponse,
    StreamingResponseWithMetadata,
    honcho_llm_call,
)
from src.telemetry import prometheus_metrics
from src.telemetry.events import DialecticCompletedEvent, emit
from src.telemetry.logging import (
    accumulate_metric,
    log_performance_metrics,
    log_token_usage_metrics,
)
from src.telemetry.prometheus.metrics import DialecticComponents, TokenTypes
from src.utils.agent_tools import (
    DIALECTIC_TOOLS,
    DIALECTIC_TOOLS_MINIMAL,
    create_tool_executor,
    search_memory,
)
from src.utils.formatting import format_new_turn_with_timestamp

logger = logging.getLogger(__name__)


def _get_dialectic_level_model_config(
    reasoning_level: ReasoningLevel,
) -> ConfiguredModelSettings:
    return settings.DIALECTIC.LEVELS[reasoning_level].MODEL_CONFIG


class DialecticAgent:
    """
    An agentic dialectic that iteratively gathers context to answer queries.

    Unlike the standard dialectic which pre-gathers all context before a single
    LLM call, this agent uses tools to strategically gather only the context
    needed to answer the specific query.
    """

    def __init__(
        self,
        workspace_name: str,
        session_name: str | None,
        observer: str,
        observed: str,
        observer_peer_card: list[str] | None = None,
        observed_peer_card: list[str] | None = None,
        metric_key: str | None = None,
        reasoning_level: ReasoningLevel = "low",
    ):
        """
        Initialize the dialectic agent.

        Args:
            workspace_name: Workspace identifier
            session_name: Session identifier (may be None for global queries)
            observer: The peer making the query
            observed: The peer being queried about
            observer_peer_card: Biographical information about the observer
            observed_peer_card: Biographical information about the observed peer
            metric_key: Optional key for logging metrics (if provided, agent won't log separately)
            reasoning_level: Level of reasoning to apply
        """
        self.workspace_name: str = workspace_name
        self.session_name: str | None = session_name
        self.observer: str = observer
        self.observed: str = observed
        self.observer_peer_card: list[str] | None = observer_peer_card
        self.observed_peer_card: list[str] | None = observed_peer_card
        self.metric_key: str | None = metric_key
        self.reasoning_level: ReasoningLevel = reasoning_level

        # Initialize conversation history with system prompt
        self.messages: list[dict[str, str]] = [
            {
                "role": "system",
                "content": prompts.agent_system_prompt(
                    observer, observed, observer_peer_card, observed_peer_card
                ),
            }
        ]
        self._session_history_initialized: bool = False
        self._prefetched_conclusion_count: int = 0
        self._run_id: str = str(uuid.uuid4())[
            :8
        ]  # Always generate for event correlation

    async def _initialize_session_history(self) -> None:
        """Fetch and inject session history into the system prompt if configured."""
        if self._session_history_initialized:
            return
        self._session_history_initialized = True

        max_tokens = settings.DIALECTIC.SESSION_HISTORY_MAX_TOKENS
        if max_tokens == 0 or not self.session_name:
            return

        # Fetch recent messages up to the token limit
        stmt = await crud.get_messages(
            workspace_name=self.workspace_name,
            session_name=self.session_name,
            token_limit=[REDACTED]
            reverse=False,  # chronological order
        )
        async with tracked_db("dialectic.session_history") as db:
            result = await db.execute(stmt)
            messages = result.scalars().all()

            if not messages:
                return

            # Format messages for injection (must access ORM attrs before session closes)
            formatted_messages: list[str] = []
            for msg in messages:
                formatted = format_new_turn_with_timestamp(
                    msg.content, msg.created_at, msg.peer_name
                )
                formatted_messages.append(formatted)

        session_history_section = (
            "\n\n## SESSION HISTORY\n\n"
            "The following is the recent conversation history from this session. "
            "Use this as immediate context when answering the query.\n\n"
            "<session_history>\n"
            f"{chr(10).join(formatted_messages)}\n"
            "</session_history>"
        )

        # Append session history to the system prompt
        self.messages[0]["content"] += session_history_section

    async def _prefetch_relevant_observations(self, query: str) -> str | None:
        """
        Prefetch semantically relevant observations for the query.

        This provides immediate context to the agent without requiring
        tool calls, improving response quality and speed.

        Performs two separate searches to prevent retrieval dilution:
        - Explicit observations (produced by deriver)
        - Higher-level observations (produced in dreaming/background/chat)

        The number of observations fetched depends on reasoning level:
        - minimal: 10 of each type (reduced context for cost savings)
        - all others: 25 of each type

        Args:
            query: The user's query

        Returns:
            Formatted observations string or None if no observations found
        """
        # Use reduced prefetch for minimal reasoning to save tokens
        prefetch_limit = 10 if self.reasoning_level == "minimal" else 25

        try:
            # Pre-compute embedding once for both searches (no DB needed)
            query_embedding = await embedding_client.embed(query)

            # search_memory manages its own short-lived DB sessions so no
            # connection is held during external vector-store calls.
            explicit_repr = await search_memory(
                workspace_name=self.workspace_name,
                observer=self.observer,
                observed=self.observed,
                query=query,
                limit=prefetch_limit,
                levels=["explicit"],
                embedding=query_embedding,
            )

            derived_repr = await search_memory(
                workspace_name=self.workspace_name,
                observer=self.observer,
                observed=self.observed,
                query=query,
                limit=prefetch_limit,
                levels=["deductive", "inductive", "contradiction"],
                embedding=query_embedding,
            )

            if explicit_repr.is_empty() and derived_repr.is_empty():
                return None

            # Count prefetched conclusions for telemetry
            explicit_count = len(explicit_repr.explicit) + len(explicit_repr.deductive)
            derived_count = len(derived_repr.explicit) + len(derived_repr.deductive)
            self._prefetched_conclusion_count = explicit_count + derived_count

            # Format as two separate sections
            parts: list[str] = []

            if not explicit_repr.is_empty():
                parts.append(explicit_repr.format_as_markdown(include_ids=False))

            if not derived_repr.is_empty():
                # Include IDs for derived so agent can use get_reasoning_chain
                parts.append(derived_repr.format_as_markdown(include_ids=True))

            return "\n".join(parts)

        except Exception as e:
            logger.warning(f"Failed to prefetch observations: {e}")
            return None

    async def _prepare_query(
        self, query: str
    ) -> tuple[Callable[[str, dict[str, Any]], Any], str, str | None, float]:
        """
        Prepare common state for answering a query.

        Handles session history initialization, metrics setup, observation prefetching,
        user message construction, and tool executor creation.

        Args:
            query: The question to answer about the peer

        Returns:
            A tuple of (tool_executor, task_name, run_id, start_time)
        """
        await self._initialize_session_history()

        run_id: str | None = None
        if self.metric_key:
            task_name = self.metric_key
        else:
            run_id = str(uuid.uuid4())[:8]
            task_name = f"dialectic_chat_{run_id}"
        start_time = time.perf_counter()

        accumulate_metric(
            task_name,
            "context",
            (
                f"workspace: {self.workspace_name}\n"
                f"session: {self.session_name or '(global)'}\n"
                f"observer: {self.observer}\n"
                f"observed: {self.observed}\n"
                f"reasoning_level: {self.reasoning_level}"
            ),
            "blob",
        )
        accumulate_metric(task_name, "query", query, "blob")

        prefetched_observations = await self._prefetch_relevant_observations(query)

        if prefetched_observations:
            user_content = (
                f"Query: {query}\n\n"
                f"## Relevant Observations (prefetched)\n"
                f"The following observations were found to be semantically relevant to your query. "
                f"Use these as primary context. You may still use tools to find additional information if needed.\n\n"
                f"{prefetched_observations}"
            )
            accumulate_metric(
                task_name, "prefetched_observations", prefetched_observations, "blob"
            )
        else:
            user_content = f"Query: {query}"

        self.messages.append({"role": "user", "content": user_content})

        tool_executor: Callable[
            [str, dict[str, Any]], Any
        ] = await create_tool_executor(
            workspace_name=self.workspace_name,
            session_name=self.session_name,
            observer=self.observer,
            observed=self.observed,
            history_token_limit=[REDACTED]
            run_id=self._run_id,
            agent_type="dialectic",
            parent_category="dialectic",
        )

        return tool_executor, task_name, run_id, start_time

    def _log_response_metrics(
        self,
        task_name: str,
        run_id: str | None,
        start_time: float,
        response_content: str,
        input_tokens: int,
        output_tokens: int,
        cache_read_input_tokens: int | None,
        cache_creation_input_tokens: int | None,
        tool_calls_count: int,
        thinking_content: str | None,
        iterations: int,
    ) -> None:
        """
        Log metrics common to both streaming and non-streaming responses.

        Args:
            task_name: Metrics task identifier
            run_id: Run identifier (None if using caller-provided metric_key)
            start_time: Start time from time.perf_counter()
            response_content: The full response text
            input_tokens: Input token count (actual from API)
            output_tokens: Output token count (actual from API)
            cache_read_input_tokens: Cache read tokens (if any)
            cache_creation_input_tokens: Cache creation tokens (if any)
            tool_calls_count: Number of tool calls made
            thinking_content: Thinking trace content (if any)
            iterations: Number of iterations in the tool execution loop
        """
        accumulate_metric(task_name, "tool_calls", tool_calls_count, "count")

        if thinking_content:
            accumulate_metric(task_name, "thinking", thinking_content, "blob")

        log_token_usage_metrics(
            task_name,
            input_tokens,
            output_tokens,
            cache_read_input_tokens or 0,
            cache_creation_input_tokens or 0,
        )
        accumulate_metric(task_name, "response", response_content, "blob")

        elapsed_ms = (time.perf_counter() - start_time) * 1000
        accumulate_metric(task_name, "total_duration", elapsed_ms, "ms")

        if not self.metric_key and run_id is not None:
            log_performance_metrics("dialectic_chat", run_id)

        # Prometheus metrics
        if settings.METRICS.ENABLED:
            prometheus_metrics.record_dialectic_tokens(
                count=input_tokens,
                token_type=[REDACTED]
                component=DialecticComponents.TOTAL.value,
                reasoning_level=self.reasoning_level,
            )
            prometheus_metrics.record_dialectic_tokens(
                count=output_tokens,
                token_type=[REDACTED]
                component=DialecticComponents.TOTAL.value,
                reasoning_level=self.reasoning_level,
            )

>sed -n '1,420p' src/dreamer/specialists.py

Chunk ID: fb2835
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 3541
Output:
"""
Agentic specialists for the dream cycle.

Each specialist is a fully autonomous agent that:
1. Receives probing questions as entry points
2. Uses tools to search for relevant observations
3. Creates new observations (deductive or inductive)
4. Can delete duplicates (deduction only)
"""

from __future__ import annotations

import logging
import time
import uuid
from abc import ABC, abstractmethod
from collections.abc import Callable
from dataclasses import dataclass
from typing import Any

from src import crud, schemas
from src.config import ConfiguredModelSettings, settings
from src.dependencies import tracked_db
from src.exceptions import ValidationException
from src.llm import HonchoLLMCallResponse, honcho_llm_call
from src.schemas import ResolvedConfiguration
from src.telemetry import prometheus_metrics
from src.telemetry.events import DreamSpecialistEvent, emit
from src.telemetry.logging import accumulate_metric, log_performance_metrics
from src.telemetry.prometheus.metrics import TokenTypes
from src.utils.agent_tools import (
    DEDUCTION_SPECIALIST_TOOLS,
    INDUCTION_SPECIALIST_TOOLS,
    create_tool_executor,
)

logger = logging.getLogger(__name__)


def _require_specialist_model_config(
    model_config: ConfiguredModelSettings | None,
    *,
    specialist_name: str,
) -> ConfiguredModelSettings:
    if model_config is None:
        raise ValidationException(
            f"{specialist_name} MODEL_CONFIG must be resolved before use"
        )
    return model_config


@dataclass
class SpecialistResult:
    """Result of a specialist run for telemetry and aggregation."""

    run_id: str
    specialist_type: str
    iterations: int
    tool_calls_count: int
    input_tokens: int
    output_tokens: int
    duration_ms: float
    success: bool
    content: str


# Tool names to exclude when peer card creation is disabled
PEER_CARD_TOOL_NAMES = {"update_peer_card"}


class BaseSpecialist(ABC):
    """Base class for agentic specialists."""

    name: str = "base"
    # Subclasses can override to customize the peer card update instruction
    peer_card_update_instruction: str = (
        "Only update this with durable profile facts via `update_peer_card`."
    )

    @abstractmethod
    def get_tools(self, *, peer_card_enabled: bool = True) -> list[dict[str, Any]]:
        """Get the tools available to this specialist."""
        ...

    @abstractmethod
    def get_model_config(self) -> ConfiguredModelSettings:
        """Get the configured model to use for this specialist."""
        ...

    def get_max_tokens(self) -> int:
        """Get max output tokens for this specialist."""
        return 16384

    def get_max_iterations(self) -> int:
        """Get max tool iterations."""
        return 15

    @abstractmethod
    def build_system_prompt(
        self, observed: str, *, peer_card_enabled: bool = True
    ) -> str:
        """Build the system prompt for this specialist."""
        ...

    @abstractmethod
    def build_user_prompt(
        self,
        hints: list[str] | None,
        peer_card: list[str] | None = None,
    ) -> str:
        """Build the user prompt with optional exploration hints and current peer card."""
        ...

    def _build_peer_card_context(self, peer_card: list[str] | None) -> str:
        """Build the peer card context section for user prompts."""
        if not peer_card:
            return ""
        facts = "\n".join(f"- {fact}" for fact in peer_card)
        return f"""
## CURRENT PEER CARD

{facts}

{self.peer_card_update_instruction}
If you update it, send the full deduplicated list and remove stale entries.

"""

    async def run(
        self,
        workspace_name: str,
        observer: str,
        observed: str,
        session_name: str | None,
        hints: list[str] | None = None,
        configuration: ResolvedConfiguration | None = None,
        parent_run_id: str | None = None,
    ) -> SpecialistResult:
        """
        Run the specialist agent.

        Uses short-lived DB sessions to avoid holding connections during LLM calls.

        Args:
            workspace_name: Workspace identifier
            observer: The observing peer
            observed: The peer being observed
            session_name: Session identifier
            hints: Optional hints to guide exploration (specialists explore freely if None)
            configuration: Resolved configuration for checking feature flags (optional)
            parent_run_id: Optional run_id from orchestrator for correlation

        Returns:
            SpecialistResult with metrics and content
        """
        run_id = parent_run_id or str(uuid.uuid4())[:8]
        task_name = f"dreamer_{self.name}_{run_id}"
        start_time = time.perf_counter()

        # Short-lived DB session for preflight operations
        async with tracked_db("dream.specialist.preflight") as db:
            await crud.get_peer(db, workspace_name, schemas.PeerCreate(name=observer))
            if observer != observed:
                await crud.get_peer(
                    db, workspace_name, schemas.PeerCreate(name=observed)
                )

            # Determine if peer card tools should be included
            peer_card_enabled = configuration is None or configuration.peer_card.create

            # Fetch current peer card to inject into prompt (saves a tool call)
            current_peer_card: list[str] | None = None
            if peer_card_enabled:
                current_peer_card = await crud.get_peer_card(
                    db,
                    workspace_name=workspace_name,
                    observer=observer,
                    observed=observed,
                )
        # DB session closed — LLM calls happen without holding a connection

        # Build messages
        messages: list[dict[str, str]] = [
            {
                "role": "system",
                "content": self.build_system_prompt(
                    observed, peer_card_enabled=peer_card_enabled
                ),
            },
            {
                "role": "user",
                "content": self.build_user_prompt(hints, current_peer_card),
            },
        ]

        # Create tool executor with telemetry context
        tool_executor: Callable[
            [str, dict[str, Any]], Any
        ] = await create_tool_executor(
            workspace_name=workspace_name,
            observer=observer,
            observed=observed,
            session_name=session_name,
            include_observation_ids=True,
            history_token_limit=[REDACTED]
            configuration=configuration,
            run_id=run_id,
            agent_type=self.name,
            parent_category="dream",
        )

        model_config = self.get_model_config()

        # Respect operator-configured max_output_tokens on the specialist's
        # ModelConfig (e.g. DREAM_DEDUCTION_MODEL_CONFIG__MAX_OUTPUT_TOKENS).
        # Only fall back to the specialist's hardcoded default when the
        # config leaves max_output_tokens unset or non-positive.
        configured_max = model_config.max_output_tokens
        effective_max_tokens = (
            configured_max
            if configured_max and configured_max > 0
            else self.get_max_tokens()
        )

        # Track iterations via callback
        iteration_count = 0

        def iteration_callback(data: Any) -> None:
            nonlocal iteration_count
            iteration_count = data.iteration

        # Run the agent loop
        response: HonchoLLMCallResponse[str] = await honcho_llm_call(
            model_config=model_config,
            prompt="",  # Ignored since we pass messages
            max_tokens=[REDACTED]
            tools=self.get_tools(peer_card_enabled=peer_card_enabled),
            tool_choice=None,
            tool_executor=tool_executor,
            max_tool_iterations=self.get_max_iterations(),
            messages=messages,
            track_name=f"Dreamer/{self.name}",
            iteration_callback=iteration_callback,
        )

        # Log metrics
        duration_ms = (time.perf_counter() - start_time) * 1000
        accumulate_metric(task_name, "total_duration", duration_ms, "ms")
        accumulate_metric(
            task_name, "tool_calls", len(response.tool_calls_made), "count"
        )
        accumulate_metric(task_name, "input_tokens", response.input_tokens, "count")
        accumulate_metric(task_name, "output_tokens", response.output_tokens, "count")

        # Prometheus metrics
        if settings.METRICS.ENABLED:
            prometheus_metrics.record_dreamer_tokens(
                count=response.input_tokens,
                specialist_name=self.name,
                token_type=[REDACTED]
            )
            prometheus_metrics.record_dreamer_tokens(
                count=response.output_tokens,
                specialist_name=self.name,
                token_type=[REDACTED]
            )

        logger.info(
            f"{self.name}: Completed in {duration_ms:.0f}ms, "
            + f"{len(response.tool_calls_made)} tool calls, "
            + f"{response.input_tokens} in / {response.output_tokens} out"
        )

        log_performance_metrics(f"dreamer_{self.name}", run_id)

        # Emit telemetry event
        emit(
            DreamSpecialistEvent(
                run_id=run_id,
                specialist_type=self.name,
                workspace_name=workspace_name,
                observer=observer,
                observed=observed,
                iterations=iteration_count,
                tool_calls_count=len(response.tool_calls_made),
                input_tokens=[REDACTED]
                output_tokens=[REDACTED]
                duration_ms=duration_ms,
                success=True,
            )
        )

        return SpecialistResult(
            run_id=run_id,
            specialist_type=self.name,
            iterations=iteration_count,
            tool_calls_count=len(response.tool_calls_made),
            input_tokens=[REDACTED]
            output_tokens=[REDACTED]
            duration_ms=duration_ms,
            success=True,
            content=response.content,
        )


class DeductionSpecialist(BaseSpecialist):
    """
    Creates deductive observations from explicit observations.

    This specialist:
    1. Explores recent observations and messages to understand what's there
    2. Identifies logical implications, knowledge updates, and contradictions
    3. Creates new deductive observations with premise linkage
    4. Deletes outdated observations
    5. Updates peer card with biographical facts
    """

    name: str = "deduction"
    peer_card_update_instruction: str = "Update this with `update_peer_card` only for stable biographical/profile facts."

    def get_tools(self, *, peer_card_enabled: bool = True) -> list[dict[str, Any]]:
        if peer_card_enabled:
            return DEDUCTION_SPECIALIST_TOOLS
        return [
            t
            for t in DEDUCTION_SPECIALIST_TOOLS
            if t["name"] not in PEER_CARD_TOOL_NAMES
        ]

    def get_model_config(self) -> ConfiguredModelSettings:
        return _require_specialist_model_config(
            settings.DREAM.DEDUCTION_MODEL_CONFIG,
            specialist_name="DREAM DEDUCTION",
        )

    def get_max_tokens(self) -> int:
        return 8192

    def get_max_iterations(self) -> int:
        return 12

    def build_system_prompt(
        self, observed: str, *, peer_card_enabled: bool = True
    ) -> str:
        peer_card_section = ""
        if peer_card_enabled:
            peer_card_section = """

## PEER CARD (REQUIRED)

The peer card is a summary of stable biographical facts. You MUST update it when you learn:
- Name, age, location, occupation
- Family members and relationships
- Standing instructions ("call me X", "don't mention Y")
- Core preferences and traits

Never add temporary event summaries, one-off conclusions, reasoning traces, or contradiction notes.

Format entries as:
- Plain facts: "Name: Alice", "Works at Google", "Lives in NYC"
- `INSTRUCTION: ...` for standing instructions
- `PREFERENCE: ...` for preferences
- `TRAIT: ...` for personality traits

Call `update_peer_card` with the complete updated list when you have new biographical info.
Keep it concise (max 40 entries), deduplicated, and current."""

        return f"""You are a deductive reasoning agent analyzing observations about {observed}.

## YOUR JOB

Create deductive observations by finding logical implications in what's already known. Think like a detective connecting evidence.

## PHASE 1: DISCOVERY

Explore what's actually in memory. Use these tools freely:
- `get_recent_observations` - See what's been learned recently
- `search_memory` - Search for specific topics
- `search_messages` - See actual conversation content

Spend a few tool calls understanding the landscape before creating anything.

## PHASE 2: ACTION

Once you understand what's there, create observations and clean up:

### Knowledge Updates (HIGH PRIORITY)
When the same fact has different values at different times:
- "meeting Tuesday" [old] → "meeting moved to Thursday" [new]
- Create a deductive update observation
- DELETE the outdated observation immediately

### Logical Implications
Extract implicit information:
- "works as SWE at Google" → "has software engineering skills", "employed in tech"
- "has kids ages 5 and 8" → "is a parent", "has school-age children"

### Contradictions
When statements can't both be true (not just updates), flag them:
- "I love coffee" vs "I hate coffee" → contradiction observation
{peer_card_section}

## CREATING OBSERVATIONS

Use `create_observations_deductive`.

```json
{{
  "observations": [{{
    "content": "The logical conclusion",
    "source_ids": ["id1", "id2"],
    "premises": ["premise 1 text", "premise 2 text"]
  }}]
}}
```

## RULES

1. Don't explain your reasoning - just call tools
2. Create observations based on what you ACTUALLY FIND, not what you expect

>sed -n '420,900p' src/dreamer/specialists.py

Chunk ID: e4df85
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1498
Output:
2. Create observations based on what you ACTUALLY FIND, not what you expect
3. Always include source_ids linking to the observations you're synthesizing
4. Empty or missing source_ids will be rejected
5. Delete outdated observations - don't leave duplicates
6. Quality over quantity - fewer good deductions beat many weak ones"""

    def build_user_prompt(
        self,
        hints: list[str] | None,
        peer_card: list[str] | None = None,
    ) -> str:
        peer_card_context = self._build_peer_card_context(peer_card)

        if hints:
            hints_str = "\n".join(f"- {q}" for q in hints[:5])
            return f"""{peer_card_context}Start by exploring recent observations and messages. These topics may be worth investigating:

{hints_str}

But follow the evidence - if you find something more interesting, pursue that instead.

Begin with `get_recent_observations` to see what's there."""

        return f"""{peer_card_context}Explore the observation space and create deductive observations.

Start with `get_recent_observations` to see what's been learned recently, then investigate whatever seems most promising.

Look for:
1. Knowledge updates (same fact, different values over time)
2. Logical implications that haven't been made explicit
3. Contradictions that need flagging

Go."""


class InductionSpecialist(BaseSpecialist):
    """
    Creates inductive observations from explicit and deductive observations.

    This specialist:
    1. Explores observations to understand what's there
    2. Identifies patterns and generalizations across multiple observations
    3. Creates new inductive observations with source linkage
    4. Updates peer card with high-confidence traits and tendencies
    """

    name: str = "induction"
    peer_card_update_instruction: str = "Only add highly stable profile traits/preferences; do not copy transient conclusions."

    def get_tools(self, *, peer_card_enabled: bool = True) -> list[dict[str, Any]]:
        if peer_card_enabled:
            return INDUCTION_SPECIALIST_TOOLS
        return [
            t
            for t in INDUCTION_SPECIALIST_TOOLS
            if t["name"] not in PEER_CARD_TOOL_NAMES
        ]

    def get_model_config(self) -> ConfiguredModelSettings:
        return _require_specialist_model_config(
            settings.DREAM.INDUCTION_MODEL_CONFIG,
            specialist_name="DREAM INDUCTION",
        )

    def get_max_tokens(self) -> int:
        return 8192

    def get_max_iterations(self) -> int:
        return 10

    def build_system_prompt(
        self, observed: str, *, peer_card_enabled: bool = True
    ) -> str:
        peer_card_section = ""
        if peer_card_enabled:
            peer_card_section = """

## PEER CARD (REQUIRED)

After identifying patterns, only update the peer card for durable profile-level traits/preferences:
- `TRAIT: Analytical thinker`
- `TRAIT: Tends to reschedule when stressed`
- `PREFERENCE: Prefers detailed explanations`

Do NOT add temporary patterns, episode-specific conclusions, or reasoning summaries.
Call `update_peer_card` with the complete deduplicated list only when a durable profile update is warranted.
Keep it concise (max 40 entries)."""

        return f"""You are an inductive reasoning agent identifying patterns about {observed}.

## YOUR JOB

Create inductive observations by finding patterns across multiple observations. Think like a psychologist identifying behavioral tendencies.

## PHASE 1: DISCOVERY

Explore broadly to find patterns. Use these tools:
- `get_recent_observations` - Recent learnings
- `search_memory` - Topic-specific search
- `search_messages` - Actual conversation content

Look at BOTH explicit observations AND deductive ones. Patterns often emerge from synthesizing across both levels.

## PHASE 2: ACTION

Create inductive observations when you see patterns:

### Behavioral Patterns
- "Tends to reschedule meetings when stressed"
- "Makes decisions after consulting with partner"
- "Projects follow: enthusiasm → doubt → completion"

### Preferences
- "Prefers morning meetings"
- "Likes detailed technical explanations"

### Personality Traits
- "Generally optimistic about outcomes"
- "Detail-oriented in planning"

### Temporal Patterns
- "Career goals have remained consistent"
- "Living situation changes frequently"
{peer_card_section}

## CREATING OBSERVATIONS

Use `create_observations_inductive`.

```json
{{
  "observations": [{{
    "content": "The pattern or generalization",
    "source_ids": ["id1", "id2", "id3"],
    "sources": ["evidence 1", "evidence 2"],
    "pattern_type": "tendency",  // preference|behavior|personality|tendency|correlation
    "confidence": "medium"  // low (2 sources), medium (3-4), high (5+)
  }}]
}}
```

## RULES

1. Minimum 2 source observations required - patterns need evidence
2. Don't just restate a single fact as a pattern
3. Confidence based on evidence count: 2=low, 3-4=medium, 5+=high
4. Look for HOW things change over time, not just static facts
5. Include source_ids - always link back to evidence
6. Empty or missing source_ids will be rejected"""

    def build_user_prompt(
        self,
        hints: list[str] | None,
        peer_card: list[str] | None = None,
    ) -> str:
        peer_card_context = self._build_peer_card_context(peer_card)

        if hints:
            hints_str = "\n".join(f"- {q}" for q in hints[:5])
            return f"""{peer_card_context}Explore and find patterns. These areas may be worth investigating:

{hints_str}

But follow the evidence - if you find patterns elsewhere, pursue those.

Start with `get_recent_observations`."""

        return f"""{peer_card_context}Explore the observation space and identify patterns.

Remember: patterns need 2+ sources. Look for tendencies, preferences, and behavioral regularities.

Go."""


# Singleton instances
SPECIALISTS: dict[str, BaseSpecialist] = {
    "deduction": DeductionSpecialist(),
    "induction": InductionSpecialist(),
}

>sed -n '1,260p' src/utils/agent_tools.py

Chunk ID: 32efae
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2257
Output:
import asyncio
import logging
import weakref
from collections.abc import Callable
from dataclasses import dataclass
from datetime import datetime
from typing import Any, cast

from pydantic import ValidationError
from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession

from src import crud, models, schemas
from src.config import settings
from src.dependencies import tracked_db
from src.embedding_client import embedding_client
from src.models import Document
from src.schemas import ResolvedConfiguration
from src.telemetry.events import (
    AgentToolConclusionsCreatedEvent,
    AgentToolConclusionsDeletedEvent,
    AgentToolPeerCardUpdatedEvent,
    emit,
)
from src.utils import summarizer
from src.utils.formatting import format_new_turn_with_timestamp, utc_now_iso
from src.utils.representation import Representation
from src.utils.types import get_current_iteration

logger = logging.getLogger(__name__)

# Hard cap to prevent unbounded peer card growth from repeated agent updates.
MAX_PEER_CARD_FACTS = 40


def _normalized_observation_input(
    obs: schemas.ObservationInput,
) -> schemas.ObservationInput:
    """Return an observation input with content normalized for persistence/embedding."""
    return obs.model_copy(update={"content": obs.content.strip()})


def _base_observation_properties() -> dict[str, Any]:
    return {
        "content": {
            "type": "string",
            "description": "The observation content",
        },
        "level": {
            "type": "string",
            "enum": [
                "explicit",
                "deductive",
                "inductive",
                "contradiction",
            ],
            "description": (
                "Level: 'explicit' for direct facts, 'deductive' for logical "
                + "necessities, 'inductive' for patterns, 'contradiction' for "
                + "conflicting statements"
            ),
        },
        "source_ids": {
            "type": "array",
            "items": {"type": "string"},
            "description": (
                "Document IDs of source or premise observations. Required and "
                + "must be non-empty for deductive, inductive, and contradiction "
                + "observations."
            ),
        },
        "premises": {
            "type": "array",
            "items": {"type": "string"},
            "description": "(For deductive) Human-readable premise text for display",
        },
        "sources": {
            "type": "array",
            "items": {"type": "string"},
            "description": "(For inductive/contradiction) Human-readable source text for display",
        },
        "pattern_type": {
            "type": "string",
            "enum": [
                "preference",
                "behavior",
                "personality",
                "tendency",
                "correlation",
            ],
            "description": "(For inductive only) Type of pattern being identified",
        },
        "confidence": {
            "type": "string",
            "enum": ["high", "medium", "low"],
            "description": (
                "(For inductive only) Confidence level: 'high' for 5+ sources, "
                + "'medium' for 3-4, 'low' for 2"
            ),
        },
    }


def _generic_observation_item_schema() -> dict[str, Any]:
    return {
        "type": "object",
        "properties": _base_observation_properties(),
        "required": ["content", "level"],
        "additionalProperties": False,
        "allOf": [
            {
                "if": {"properties": {"level": {"const": "deductive"}}},
                "then": {
                    "required": ["source_ids", "premises"],
                    "properties": {
                        "source_ids": {
                            "type": "array",
                            "items": {"type": "string"},
                            "minItems": 1,
                        },
                        "premises": {
                            "type": "array",
                            "items": {"type": "string"},
                            "minItems": 1,
                        },
                    },
                },
            },
            {
                "if": {"properties": {"level": {"const": "inductive"}}},
                "then": {
                    "required": [
                        "source_ids",
                        "sources",
                        "pattern_type",
                        "confidence",
                    ],
                    "properties": {
                        "source_ids": {
                            "type": "array",
                            "items": {"type": "string"},
                            "minItems": 2,
                        },
                        "sources": {
                            "type": "array",
                            "items": {"type": "string"},
                            "minItems": 2,
                        },
                    },
                },
            },
            {
                "if": {"properties": {"level": {"const": "contradiction"}}},
                "then": {
                    "required": ["source_ids", "sources"],
                    "properties": {
                        "source_ids": {
                            "type": "array",
                            "items": {"type": "string"},
                            "minItems": 2,
                        },
                        "sources": {
                            "type": "array",
                            "items": {"type": "string"},
                            "minItems": 2,
                        },
                    },
                },
            },
        ],
    }


def _deductive_observation_item_schema() -> dict[str, Any]:
    return {
        "type": "object",
        "properties": {
            "content": {
                "type": "string",
                "description": "The deductive conclusion as a self-contained statement",
            },
            "source_ids": {
                "type": "array",
                "items": {"type": "string"},
                "minItems": 1,
                "description": "Required non-empty list of source observation IDs supporting the deduction",
            },
            "premises": {
                "type": "array",
                "items": {"type": "string"},
                "minItems": 1,
                "description": "Required human-readable premise text matching the source observations",
            },
        },
        "required": ["content", "source_ids", "premises"],
        "additionalProperties": False,
    }


def _inductive_observation_item_schema() -> dict[str, Any]:
    return {
        "type": "object",
        "properties": {
            "content": {
                "type": "string",
                "description": "The inductive pattern or generalization as a self-contained statement",
            },
            "source_ids": {
                "type": "array",
                "items": {"type": "string"},
                "minItems": 2,
                "description": "Required list of at least two source observation IDs supporting the pattern",
            },
            "sources": {
                "type": "array",
                "items": {"type": "string"},
                "minItems": 2,
                "description": "Required human-readable evidence text matching the source observations",
            },
            "pattern_type": {
                "type": "string",
                "enum": [
                    "preference",
                    "behavior",
                    "personality",
                    "tendency",
                    "correlation",
                ],
                "description": "Required pattern category",
            },
            "confidence": {
                "type": "string",
                "enum": ["high", "medium", "low"],
                "description": "Required confidence level based on evidence count",
            },
        },
        "required": ["content", "source_ids", "sources", "pattern_type", "confidence"],
        "additionalProperties": False,
    }


def _safe_int(value: Any, default: int) -> int:
    """Coerce a tool input value to int, returning default on failure.

    LLMs sometimes pass non-numeric strings (e.g. 'Infinity') for integer
    parameters which would crash ``min()`` comparisons.
    """
    try:
        return int(value)
    except (TypeError, ValueError, OverflowError):
        return default


# Module-level lock registry for thread-safe observation creation.
# Keyed by (workspace_name, observer, observed) to ensure all tool executors
# operating on the same data share the same lock.
#
# Uses WeakValueDictionary so entries are automatically removed when no
# ToolContext holds a reference to the lock (i.e., all executors for that
# key have finished and been garbage collected). This prevents unbounded

>sed -n '260,620p' src/utils/agent_tools.py

Chunk ID: 4b2f04
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 3666
Output:
# key have finished and been garbage collected). This prevents unbounded
# growth over the lifetime of a long-running deriver process.
_observation_locks: weakref.WeakValueDictionary[tuple[str, str, str], asyncio.Lock] = (
    weakref.WeakValueDictionary()
)
_registry_lock = asyncio.Lock()


async def get_observation_lock(
    workspace_name: str, observer: str, observed: str
) -> asyncio.Lock:
    """
    Get or create a lock for a specific workspace/observer/observed combination.

    This ensures that concurrent tool executors operating on the same observation
    space share a lock, preventing race conditions during document creation.

    The lock is stored as a weak reference — it stays alive as long as at least
    one ToolContext (via create_tool_executor) holds a strong reference. Once all
    executors for a key finish and are garbage collected, the entry is
    automatically removed from the registry.

    Args:
        workspace_name: Workspace identifier
        observer: The observing peer
        observed: The peer being observed

    Returns:
        An asyncio.Lock shared by all executors for this combination
    """
    key = (workspace_name, observer, observed)
    async with _registry_lock:
        lock = _observation_locks.get(key)
        if lock is None:
            lock = asyncio.Lock()
            _observation_locks[key] = lock
        return lock


@dataclass
class ObservationFailure:
    """Records a single observation that failed during creation."""

    content_preview: str
    error: str


@dataclass
class ObservationsCreatedResult:
    """Result of a batch create_observations call."""

    created_count: int
    created_levels: list[str]
    failed: list[ObservationFailure]


def _truncate_tool_output(output: str, max_chars: int | None = None) -> str:
    """Truncate tool output to prevent token explosion."""
    if max_chars is None:
        max_chars = settings.LLM.MAX_TOOL_OUTPUT_CHARS
    if len(output) <= max_chars:
        return output
    truncated = output[:max_chars]
    return (
        truncated
        + f"\n\n[OUTPUT TRUNCATED - showing {max_chars:,} of {len(output):,} characters]"
    )


def _truncate_message_content(content: str, max_chars: int | None = None) -> str:
    """Truncate individual message content (simple beginning truncation)."""
    if max_chars is None:
        max_chars = settings.LLM.MAX_MESSAGE_CONTENT_CHARS
    if len(content) <= max_chars:
        return content
    return content[:max_chars] + "..."


def _extract_pattern_snippet(
    content: str, pattern: str, max_chars: int | None = None
) -> str:
    """Extract snippet around a regex pattern match.

    For grep/exact text search, finds the pattern and extracts context around it.
    """
    import re

    if max_chars is None:
        max_chars = settings.LLM.MAX_MESSAGE_CONTENT_CHARS
    if len(content) <= max_chars:
        return content

    match = re.search(re.escape(pattern), content, re.IGNORECASE)
    if not match:
        # No match, return beginning
        return content[:max_chars] + "..."

    match_start = match.start()
    match_end = match.end()

    # Calculate window around match
    match_len = match_end - match_start
    remaining = max_chars - match_len
    before = remaining // 2
    after = remaining - before

    start = max(0, match_start - before)
    end = min(len(content), match_end + after)

    # Adjust if we hit boundaries
    if start == 0:
        end = min(len(content), max_chars)
    elif end == len(content):
        start = max(0, len(content) - max_chars)

    snippet = content[start:end]

    prefix = "..." if start > 0 else ""
    suffix = "..." if end < len(content) else ""

    return f"{prefix}{snippet}{suffix}"


TOOLS: dict[str, dict[str, Any]] = {
    "create_observations": {
        "name": "create_observations",
        "description": "Create observations at any level: explicit (facts), deductive (logical necessities), inductive (patterns), or contradiction (conflicting statements). For deductive, inductive, and contradiction observations, missing or empty source_ids are invalid and will be rejected.",
        "input_schema": {
            "type": "object",
            "properties": {
                "observations": {
                    "type": "array",
                    "description": "List of observations to create",
                    "items": _generic_observation_item_schema(),
                },
            },
            "required": ["observations"],
        },
    },
    "create_observations_deductive": {
        "name": "create_observations_deductive",
        "description": "Create new deductive observations discovered while answering the query. Every observation must include non-empty source_ids and premise text. Use this only for novel deductions grounded in existing observations.",
        "input_schema": {
            "type": "object",
            "properties": {
                "observations": {
                    "type": "array",
                    "description": "List of new deductive observations to create",
                    "items": _deductive_observation_item_schema(),
                },
            },
            "required": ["observations"],
        },
    },
    "create_observations_inductive": {
        "name": "create_observations_inductive",
        "description": "Create new inductive observations discovered while answering the query. Every observation must include source_ids, source text, pattern_type, and confidence. Use this only for patterns supported by multiple observations.",
        "input_schema": {
            "type": "object",
            "properties": {
                "observations": {
                    "type": "array",
                    "description": "List of new inductive observations to create",
                    "items": _inductive_observation_item_schema(),
                },
            },
            "required": ["observations"],
        },
    },
    "update_peer_card": {
        "name": "update_peer_card",
        "description": (
            "Update the peer card with durable profile facts about the observed peer. "
            + "Only include stable biographical facts, standing instructions, and long-lived preferences/traits. "
            + "Do not include one-off conclusions, temporary events, or duplicate entries."
        ),
        "input_schema": {
            "type": "object",
            "properties": {
                "content": {
                    "type": "array",
                    "description": (
                        "Complete deduplicated peer card list (max 40 entries). "
                        + "Each entry should be a concise standalone profile fact."
                    ),
                    "items": {"type": "string"},
                },
            },
            "required": ["content"],
        },
    },
    "get_recent_history": {
        "name": "get_recent_history",
        "description": "Retrieve recent conversation history to get more context about the conversation.",
        "input_schema": {
            "type": "object",
            "properties": {},
        },
    },
    "search_memory": {
        "name": "search_memory",
        "description": "Search for observations in memory using semantic similarity. Use this to find relevant information about the peer when you need to recall specific details.",
        "input_schema": {
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "Search query text",
                },
                "top_k": {
                    "type": "integer",
                    "description": "(Optional) number of results to return (default: 20, max: 40)",
                    "default": 20,
                },
            },
            "required": ["query"],
        },
    },
    "get_observation_context": {
        "name": "get_observation_context",
        "description": "Retrieve messages for given message IDs along with surrounding context. Takes message IDs (from an observation's message_ids field) and retrieves those messages plus the messages immediately before and after each one to provide conversation context.",
        "input_schema": {
            "type": "object",
            "properties": {
                "message_ids": {
                    "type": "array",
                    "items": {"type": "string"},
                    "description": "List of message IDs to retrieve (get these from observation.message_ids in search results)",
                },
            },
            "required": ["message_ids"],
        },
    },
    "search_messages": {
        "name": "search_messages",
        "description": "Search for messages using semantic similarity and retrieve conversation snippets. Returns matching messages with surrounding context (2 messages before and after). Nearby matches within the same session are merged into a single snippet to avoid repetition.",
        "input_schema": {
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "Search query text to find relevant messages",
                },
                "limit": {
                    "type": "integer",
                    "description": "Maximum number of matching messages to return (default: 10, max: 20)",
                    "default": 10,
                },
            },
            "required": ["query"],
        },
    },
    "grep_messages": {
        "name": "grep_messages",
        "description": "Search for messages containing specific text (case-insensitive). Unlike semantic search, this finds EXACT text matches. Use for finding specific names, dates, phrases, or keywords mentioned in conversations. Returns messages with surrounding context.",
        "input_schema": {
            "type": "object",
            "properties": {
                "text": {
                    "type": "string",
                    "description": "Text to search for (case-insensitive substring match)",
                },
                "limit": {
                    "type": "integer",
                    "description": "Maximum messages to return (default: 10, max: 30)",
                    "default": 10,
                },
                "context_window": {
                    "type": "integer",
                    "description": "Number of messages before/after each match to include (default: 2)",
                    "default": 2,
                },
            },
            "required": ["text"],
        },
    },
    "get_messages_by_date_range": {
        "name": "get_messages_by_date_range",
        "description": "Get messages from a specific date range. Use this to find what was discussed during a particular time period, or to compare information before vs after a date. Essential for knowledge update questions.",
        "input_schema": {
            "type": "object",
            "properties": {
                "after_date": {
                    "type": "string",
                    "description": "Start date (ISO format, e.g., '2024-01-15'). Returns messages after this date.",
                },
                "before_date": {
                    "type": "string",
                    "description": "End date (ISO format). Returns messages before this date.",
                },
                "limit": {
                    "type": "integer",
                    "description": "Maximum messages to return (default: 20, max: 50)",
                    "default": 20,
                },
                "order": {
                    "type": "string",
                    "enum": ["asc", "desc"],
                    "description": "Sort order: 'asc' for oldest first, 'desc' for newest first (default: desc)",
                    "default": "desc",
                },
            },
        },
    },
    "search_messages_temporal": {
        "name": "search_messages_temporal",
        "description": "Semantic search for messages with optional date filtering. Combines the power of semantic search with time constraints. Use after_date to find recent mentions of a topic, or before_date to find what was said about something before a certain point. Best for knowledge update questions where you need to find the MOST RECENT discussion of a topic.",
        "input_schema": {
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "Semantic search query",
                },
                "after_date": {
                    "type": "string",
                    "description": "Only return messages after this date (ISO format, e.g., '2024-01-15')",
                },
                "before_date": {
                    "type": "string",
                    "description": "Only return messages before this date (ISO format)",
                },
                "limit": {
                    "type": "integer",
                    "description": "Maximum messages to return (default: 10, max: 20)",
                    "default": 10,
                },
                "context_window": {
                    "type": "integer",
                    "description": "Messages before/after each match (default: 2)",
                    "default": 2,
                },
            },
            "required": ["query"],
        },
    },
    "get_recent_observations": {
        "name": "get_recent_observations",
        "description": "Get the most recent observations about the peer. Useful for understanding what's been learned recently.",
        "input_schema": {
            "type": "object",
            "properties": {
                "limit": {
                    "type": "integer",
                    "description": "Maximum number of observations to return (default: 10)",
                    "default": 10,
                },
                "session_only": {
                    "type": "boolean",
                    "description": "If true, only return observations from the current session (default: false)",
                    "default": False,
                },
            },
        },
    },
    "get_most_derived_observations": {
        "name": "get_most_derived_observations",
        "description": "Get observations that have been reinforced most frequently across conversations. These represent the most established facts about the peer.",
        "input_schema": {
            "type": "object",
            "properties": {

>sed -n '1,260p' src/crud/peer_card.py

Chunk ID: 7dd77d
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 755
Output:
from __future__ import annotations

import logging
from typing import Any, cast

from sqlalchemy import update
from sqlalchemy.engine import CursorResult
from sqlalchemy.ext.asyncio import AsyncSession

from src import exceptions, models, schemas
from src.cache.client import safe_cache_delete
from src.crud.peer import get_or_create_peers, get_peer, peer_cache_key

logger = logging.getLogger(__name__)


async def get_peer_card(
    db: AsyncSession,
    workspace_name: str,
    *,
    observer: str,
    observed: str,
) -> list[str] | None:
    """
    Get peer card from internal_metadata.

    The peer card is returned for the observer/observed relationship.

    Args:
        db: Database session
        workspace_name: Name of the workspace
        observed: Peer name of the peer described in the peer card
        observer: Peer name of the observer

    Returns:
        The peer's card text if present, otherwise None.

    Raises:
        ResourceNotFoundException: If the peer does not exist.
    """
    peer = await get_peer(db, workspace_name, schemas.PeerCreate(name=observer))
    return cast(
        list[str] | None,
        peer.internal_metadata.get(
            construct_peer_card_label(observer=observer, observed=observed)
        ),
    )


async def set_peer_card(
    db: AsyncSession,
    workspace_name: str,
    peer_card: list[str],
    *,
    observer: str,
    observed: str,
) -> None:
    """
    Set peer card for a peer.

    If observer_name is provided, the peer card is set for the observer/observed relationship.

    Args:
        db: Database session
        workspace_name: Name of the workspace
        peer_card: List of strings to set as the peer card
        observed: Peer name of the peer described in the peer card
        observer: Peer name of the observer

    """
    # Ensure the peer exists (get-or-create)
    peers_result = await get_or_create_peers(
        db, workspace_name, [schemas.PeerCreate(name=observer)]
    )

    stmt = (
        update(models.Peer)
        .where(models.Peer.workspace_name == workspace_name)
        .where(models.Peer.name == observer)
        .values(
            internal_metadata=models.Peer.internal_metadata.op("||")(
                {
                    construct_peer_card_label(
                        observer=observer, observed=observed
                    ): peer_card
                }
            )
        )
    )
    result = cast(CursorResult[Any], await db.execute(stmt))
    if result.rowcount == 0:
        raise exceptions.ResourceNotFoundException(
            f"Peer {observer} not found in workspace {workspace_name}"
        )
    await db.commit()
    await peers_result.post_commit()

    # Invalidate cache - read-through pattern
    cache_key = peer_cache_key(workspace_name, observer)
    await safe_cache_delete(cache_key)


def construct_peer_card_label(*, observer: str, observed: str) -> str:
    if observer == observed:
        return "peer_card"
    return f"{observed}_peer_card"

>sed -n '1,260p' src/routers/messages.py

Chunk ID: 3cbdc4
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2131
Output:
import logging

from fastapi import (
    APIRouter,
    BackgroundTasks,
    Body,
    Depends,
    File,
    Form,
    Path,
    Query,
    UploadFile,
)
from fastapi_pagination import Page
from fastapi_pagination.ext.sqlalchemy import apaginate
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy.orm.attributes import flag_modified

from src import crud, schemas
from src.config import settings
from src.dependencies import db
from src.deriver import enqueue
from src.exceptions import FileTooLargeError, ResourceNotFoundException
from src.security import require_auth
from src.telemetry import prometheus_metrics
from src.utils.files import process_file_uploads_for_messages

logger = logging.getLogger(__name__)

router = APIRouter(
    prefix="/workspaces/{workspace_id}/sessions/{session_id}/messages",
    tags=["messages"],
    dependencies=[
        Depends(require_auth(workspace_name="workspace_id", session_name="session_id"))
    ],
)


async def parse_upload_form(
    peer_id: str = Form(...),
    metadata: str | None = Form(None),
    configuration: str | None = Form(None),
    created_at: str | None = Form(None),
) -> schemas.MessageUploadCreate:
    """Parse form data for file upload requests"""
    import json
    from datetime import datetime

    parsed_metadata = None
    if metadata:
        try:
            parsed_metadata = json.loads(metadata)
        except json.JSONDecodeError:
            logger.warning(f"Failed to parse metadata JSON: {metadata}")
            parsed_metadata = None

    parsed_configuration = None
    if configuration:
        try:
            parsed_configuration = json.loads(configuration)
        except json.JSONDecodeError:
            logger.warning(f"Failed to parse configuration JSON: {configuration}")
            parsed_configuration = None

    parsed_created_at = None
    if created_at:
        try:
            parsed_created_at = datetime.fromisoformat(
                created_at.replace("Z", "+00:00")
            )
        except (ValueError, AttributeError):
            logger.warning(f"Failed to parse created_at: {created_at}")
            parsed_created_at = None

    return schemas.MessageUploadCreate(
        peer_id=peer_id,
        metadata=parsed_metadata,
        configuration=parsed_configuration,
        created_at=parsed_created_at,
    )


@router.post("", response_model=list[schemas.Message], status_code=201)
@router.post(
    "/", response_model=list[schemas.Message], status_code=201, include_in_schema=False
)  # backwards compatibility with pre-2.6.0 faulty route endpoint
async def create_messages_for_session(
    background_tasks: BackgroundTasks,
    messages: schemas.MessageBatchCreate,
    workspace_id: str = Path(...),
    session_id: str = Path(...),
    db: AsyncSession = db,
):
    """Add new message(s) to a session."""
    try:
        created_messages = await crud.create_messages(
            db,
            messages=messages.messages,
            workspace_name=workspace_id,
            session_name=session_id,
        )

        # Prometheus metrics
        if settings.METRICS.ENABLED:
            prometheus_metrics.record_messages_created(
                count=len(created_messages),
                workspace_name=workspace_id,
            )

        # Enqueue for processing (existing logic)
        payloads = [
            {
                "workspace_name": workspace_id,
                "session_name": session_id,
                "message_id": message.id,
                "content": message.content,
                "peer_name": message.peer_name,
                "created_at": message.created_at,
                "message_public_id": message.public_id,
                "seq_in_session": message.seq_in_session,
                "configuration": original.configuration,
            }
            for message, original in zip(
                created_messages, messages.messages, strict=True
            )
        ]

        # Enqueue all messages in one call
        background_tasks.add_task(enqueue, payloads)

        return created_messages
    except ValueError as e:
        logger.warning(f"Failed to create messages for session {session_id}: {str(e)}")
        raise


@router.post("/upload", response_model=list[schemas.Message], status_code=201)
async def create_messages_with_file(
    background_tasks: BackgroundTasks,
    workspace_id: str = Path(...),
    session_id: str = Path(...),
    form_data: schemas.MessageUploadCreate = Depends(parse_upload_form),
    file: UploadFile = File(...),
    db: AsyncSession = db,
):
    """Create messages from uploaded files. Files are converted to text and split into multiple messages."""

    # Validate file size
    if file.size and file.size > settings.MAX_FILE_SIZE:
        raise FileTooLargeError(
            f"File size ({file.size} bytes) exceeds maximum allowed size ({settings.MAX_FILE_SIZE} bytes)",
        )

    # Process files using shared utility function
    all_message_data = await process_file_uploads_for_messages(
        file=file,
        peer_id=form_data.peer_id,
        metadata=form_data.metadata,
        configuration=form_data.configuration,
        created_at=form_data.created_at,
    )

    # Create messages
    message_creates = [item["message_create"] for item in all_message_data]
    created_messages = await crud.create_messages(
        db,
        messages=message_creates,
        workspace_name=workspace_id,
        session_name=session_id,
    )

    # Update internal_metadata for file-related messages
    for i, message in enumerate(created_messages):
        file_metadata = all_message_data[i]["file_metadata"]
        message.internal_metadata.update(file_metadata)
        flag_modified(message, "internal_metadata")

    await db.commit()

    # Enqueue for processing (same as regular messages)
    payloads = [
        {
            "workspace_name": workspace_id,
            "session_name": session_id,
            "message_id": message.id,
            "content": message.content,
            "peer_name": message.peer_name,
            "created_at": message.created_at,
            "message_public_id": message.public_id,
            "seq_in_session": message.seq_in_session,
            "configuration": form_data.configuration,
        }
        for message in created_messages
    ]

    background_tasks.add_task(enqueue, payloads)
    logger.debug(
        "Batch of %s messages created from file uploads and queued for processing",
        len(created_messages),
    )

    # Prometheus metrics
    if settings.METRICS.ENABLED:
        prometheus_metrics.record_messages_created(
            count=len(created_messages),
            workspace_name=workspace_id,
        )

    return created_messages


@router.post("/list", response_model=Page[schemas.Message])
async def get_messages(
    workspace_id: str = Path(...),
    session_id: str = Path(...),
    options: schemas.MessageGet | None = Body(
        None, description="Filtering options for the message list"
    ),
    reverse: bool | None = Query(
        False, description="Whether to reverse the order of results"
    ),
    db: AsyncSession = db,
):
    """Get all messages for a Session with optional filters. Results are paginated."""
    try:
        filters = None
        if options and hasattr(options, "filters"):
            filters = options.filters
            if filters == {}:
                filters = None

        messages_query = await crud.get_messages(
            workspace_name=workspace_id,
            session_name=session_id,
            filters=filters,
            reverse=reverse,
        )

        return await apaginate(db, messages_query)
    except ValueError as e:
        logger.warning(f"Failed to get messages for session {session_id}: {str(e)}")
        raise ResourceNotFoundException("Session not found") from e


@router.get("/{message_id}", response_model=schemas.Message)
async def get_message(
    workspace_id: str = Path(...),
    session_id: str = Path(...),
    message_id: str = Path(...),
    db: AsyncSession = db,
):
    """Get a single message by ID from a Session."""
    honcho_message = await crud.get_message(
        db, workspace_name=workspace_id, session_name=session_id, message_id=message_id
    )
    if honcho_message is None:
        logger.warning(f"Message {message_id} not found in session {session_id}")
        raise ResourceNotFoundException(f"Message with ID {message_id} not found")
    return honcho_message

>sed -n '620,1120p' src/utils/agent_tools.py

Chunk ID: fadb4e
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 4640
Output:
            "properties": {
                "limit": {
                    "type": "integer",
                    "description": "Maximum number of observations to return (default: 10)",
                    "default": 10,
                },
            },
        },
    },
    "get_session_summary": {
        "name": "get_session_summary",
        "description": "Get the session summary (short or long form). Useful for understanding the overall conversation context.",
        "input_schema": {
            "type": "object",
            "properties": {
                "summary_type": {
                    "type": "string",
                    "enum": ["short", "long"],
                    "description": "Type of summary to retrieve (default: short)",
                    "default": "short",
                },
            },
        },
    },
    "get_peer_card": {
        "name": "get_peer_card",
        "description": "Get the peer card containing known biographical information about the peer (name, age, location, etc.).",
        "input_schema": {
            "type": "object",
            "properties": {},
        },
    },
    "delete_observations": {
        "name": "delete_observations",
        "description": "Delete observations by their IDs. Use the exact ID shown in [id:xxx] format from search results. Example: if observation shows '[id:abc123XYZ]', pass 'abc123XYZ' to delete it.",
        "input_schema": {
            "type": "object",
            "properties": {
                "observation_ids": {
                    "type": "array",
                    "items": {"type": "string"},
                    "description": "List of observation IDs to delete (use the exact ID from [id:xxx] in search results)",
                },
            },
            "required": ["observation_ids"],
        },
    },
    "finish_consolidation": {
        "name": "finish_consolidation",
        "description": "Signal that consolidation is complete. Call this when you have finished your consolidation work and are ready to stop. You MUST call this tool when done - do not keep exploring indefinitely.",
        "input_schema": {
            "type": "object",
            "properties": {
                "summary": {
                    "type": "string",
                    "description": "Brief summary of what was accomplished (peer card updates, observations consolidated, observations deleted)",
                },
            },
            "required": ["summary"],
        },
    },
    "extract_preferences": {
        "name": "extract_preferences",
        "description": "Extract user preferences and standing instructions from conversation history. This tool performs both semantic and text searches for preferences, instructions, and communication style preferences, then returns them for adding to the peer card. Call this FIRST during consolidation.",
        "input_schema": {
            "type": "object",
            "properties": {},
        },
    },
    "get_reasoning_chain": {
        "name": "get_reasoning_chain",
        "description": "Get the reasoning chain for an observation - traverse the tree to find premises (for deductive) or sources (for inductive), and/or find conclusions derived from this observation. Use this to understand how an observation was derived or what conclusions depend on it.",
        "input_schema": {
            "type": "object",
            "properties": {
                "observation_id": {
                    "type": "string",
                    "description": "The document ID of the observation to get the reasoning chain for",
                },
                "direction": {
                    "type": "string",
                    "enum": ["premises", "conclusions", "both"],
                    "description": "'premises' to get what this observation is based on, 'conclusions' to get what depends on it, 'both' for full context",
                    "default": "both",
                },
            },
            "required": ["observation_id"],
        },
    },
}

# Tools for the dialectic agent (analysis)
DIALECTIC_TOOLS: list[dict[str, Any]] = [
    TOOLS["search_memory"],
    TOOLS["search_messages"],
    TOOLS["get_observation_context"],
    # TOOLS["create_observations_deductive"],
    TOOLS["grep_messages"],  # For exact text search (names, dates, keywords)
    TOOLS["get_messages_by_date_range"],  # For temporal/date-based queries
    TOOLS["search_messages_temporal"],  # Semantic search + date filtering
    TOOLS["get_reasoning_chain"],  # Traverse reasoning trees
]

# Minimal tools for dialectic agent at "minimal" reasoning level
# Reduces cost by limiting tool definitions in context
DIALECTIC_TOOLS_MINIMAL: list[dict[str, Any]] = [
    TOOLS["search_memory"],
    TOOLS["search_messages"],
]

# Tools for the dreamer agent (consolidation + peer card + deduplication)
DREAMER_TOOLS: list[dict[str, Any]] = [
    # Preference extraction (should be called first)
    TOOLS["extract_preferences"],
    TOOLS["get_recent_observations"],
    TOOLS["get_most_derived_observations"],
    TOOLS["search_memory"],
    TOOLS["get_peer_card"],
    TOOLS["create_observations"],
    TOOLS["delete_observations"],
    TOOLS["update_peer_card"],
    # Message access tools for context verification
    TOOLS["search_messages"],
    TOOLS["get_observation_context"],
    # Tree traversal
    TOOLS["get_reasoning_chain"],
    # Completion signal
    TOOLS["finish_consolidation"],
]

# Tools for the deduction specialist (dreamer phase 1)
# Creates deductive observations from explicit observations, can delete duplicates
# Includes message access for context and self-directed exploration
# Note: get_peer_card is not included - peer card is injected into the prompt directly
DEDUCTION_SPECIALIST_TOOLS: list[dict[str, Any]] = [
    # Discovery tools
    TOOLS["get_recent_observations"],
    TOOLS["search_memory"],
    TOOLS["search_messages"],
    # Action tools
    TOOLS["create_observations_deductive"],
    TOOLS["delete_observations"],
    TOOLS["update_peer_card"],
]

# Tools for the induction specialist (dreamer phase 2)
# Creates inductive observations from explicit and deductive observations
# Includes message access for context and self-directed exploration
# Note: get_peer_card is not included - peer card is injected into the prompt directly
INDUCTION_SPECIALIST_TOOLS: list[dict[str, Any]] = [
    # Discovery tools
    TOOLS["get_recent_observations"],
    TOOLS["search_memory"],
    TOOLS["search_messages"],
    # Action tools
    TOOLS["create_observations_inductive"],
    TOOLS["update_peer_card"],
]


async def create_observations(
    observations: list[schemas.ObservationInput],
    observer: str,
    observed: str,
    session_name: str | None,
    workspace_name: str,
    message_ids: list[int],
    message_created_at: str,
) -> ObservationsCreatedResult:
    """
    Create multiple observations (documents) in the memory system in a single call.

    Uses short-lived DB sessions to avoid holding connections during embedding API calls.

    Args:
        observations: List of validated observation inputs
        observer: The peer making the observation
        observed: The peer being observed
        session_name: Session identifier
        workspace_name: Workspace identifier
        message_ids: List of message IDs these observations are based on
        message_created_at: Timestamp of the message that triggered these observations

    Returns:
        ObservationsCreatedResult with created count and any per-observation failures
    """
    if not observations:
        logger.warning("create_observations called with empty list")
        return ObservationsCreatedResult(created_count=0, created_levels=[], failed=[])

    normalized_observations = [
        _normalized_observation_input(obs) for obs in observations if obs.content.strip()
    ]
    if not normalized_observations:
        logger.info("No non-empty observations to create")
        return ObservationsCreatedResult(created_count=0, created_levels=[], failed=[])

    # Phase 1: Ensure collection exists (short DB scope)
    async with tracked_db("create_observations.collection") as db:
        await crud.get_or_create_collection(
            db,
            workspace_name,
            observer=observer,
            observed=observed,
        )

    # Phase 2: Compute embeddings (no DB needed)
    contents = [obs.content for obs in normalized_observations]
    embeddings_by_index: dict[int, list[float]] | None = None
    try:
        embeddings = await embedding_client.simple_batch_embed(contents)
        embeddings_by_index = dict(
            zip(range(len(normalized_observations)), embeddings, strict=True)
        )
    except Exception as e:
        logger.warning(
            "Batch embedding failed for create_observations; falling back to per-observation embedding: %s",
            e,
        )

    # Build document objects with pre-computed embeddings
    documents: list[schemas.DocumentCreate] = []
    failed: list[ObservationFailure] = []
    for i, obs in enumerate(normalized_observations):
        embedding: list[float]
        if embeddings_by_index is not None:
            embedding = embeddings_by_index[i]
        else:
            try:
                embedding = await embedding_client.embed(obs.content)
            except Exception as e:
                logger.warning(
                    "Error embedding observation content for level '%s': %s",
                    obs.level,
                    e,
                )
                failed.append(
                    ObservationFailure(
                        content_preview=obs.content[:50],
                        error=f"Embedding failed: {e}",
                    )
                )
                continue

        # Build metadata with level-specific fields
        metadata = schemas.DocumentMetadata(
            message_ids=message_ids,
            message_created_at=message_created_at,
            source_ids=obs.source_ids
            if obs.level in ("deductive", "inductive", "contradiction")
            else None,
            premises=obs.premises if obs.level == "deductive" else None,
            sources=obs.sources
            if obs.level in ("inductive", "contradiction")
            else None,
            pattern_type=obs.pattern_type if obs.level == "inductive" else None,
            confidence=(obs.confidence or "medium")
            if obs.level == "inductive"
            else None,
        )

        doc = schemas.DocumentCreate(
            content=obs.content,
            session_name=session_name,
            level=obs.level,
            metadata=metadata,
            embedding=embedding,
            source_ids=obs.source_ids
            if obs.level in ("deductive", "inductive", "contradiction")
            else None,
        )
        documents.append(doc)

    # Phase 3: Bulk create all documents (short DB scope)
    accepted: list[schemas.DocumentCreate] = []
    if documents:
        async with tracked_db("create_observations.save") as db:
            accepted = await crud.create_documents(
                db,
                documents=documents,
                workspace_name=workspace_name,
                observer=observer,
                observed=observed,
                deduplicate=True,
            )
        logger.info(
            "Created %d observations in %s/%s/%s",
            len(accepted),
            workspace_name,
            observer,
            observed,
        )

    return ObservationsCreatedResult(
        created_count=len(accepted),
        created_levels=[doc.level for doc in accepted],
        failed=failed,
    )


async def get_recent_history(
    db: AsyncSession,
    workspace_name: str,
    session_name: str | None,
    observed: str | None = None,
    token_limit: int = 8192,
) -> list[models.Message]:
    """
    Retrieve recent conversation history.

    If session_name is provided, retrieves messages from that session.
    If session_name is None but observed is provided, retrieves recent messages
    sent by the observed peer across all their sessions.

    Args:
        db: Database session
        workspace_name: Workspace identifier
        session_name: Session identifier (optional)
        observed: Peer name to filter by when no session specified (optional)
        token_limit: Maximum tokens to retrieve (default: 8192)

    Returns:
        List of messages in chronological order
    """
    if session_name:
        # Get messages from a specific session
        messages_stmt = await crud.get_messages(
            workspace_name=workspace_name,
            session_name=session_name,
            token_limit=[REDACTED]
            reverse=True,  # Get most recent first
        )
        result = await db.execute(messages_stmt)
        messages = result.scalars().all()
        # Return in chronological order
        return list(reversed(messages))
    elif observed:
        # Get recent messages from the observed peer across all sessions
        stmt = (
            select(models.Message)
            .where(models.Message.workspace_name == workspace_name)
            .where(models.Message.peer_name == observed)
            .order_by(models.Message.created_at.desc())
            .limit(50)  # Limit to recent messages
        )
        result = await db.execute(stmt)
        messages = list(result.scalars().all())
        # Return in chronological order
        return list(reversed(messages))
    else:
        # No session and no observed peer - can't retrieve history
        return []


async def search_memory(
    workspace_name: str,
    observer: str,
    observed: str,
    query: str,
    limit: int,
    levels: list[str] | None = None,
    embedding: list[float] | None = None,
) -> Representation:
    """
    Search for observations in memory using semantic similarity.

    Does not require a DB session — ``query_documents`` manages its own
    short-lived sessions so no connection is held during external calls.

    Args:
        workspace_name: Workspace identifier
        observer: The peer who made the observations
        observed: The peer who was observed
        query: Search query text
        limit: Maximum number of results
        levels: Optional list of observation levels to filter by
                (e.g., ["explicit"], ["deductive", "inductive", "contradiction"])
        embedding: Optional pre-computed embedding to avoid redundant API calls

    Returns:
        Representation object containing relevant observations
    """
    # Build filter for levels if specified
    filters: dict[str, Any] | None = None
    if levels:
        filters = {"level": {"in": levels}}

    documents = await crud.query_documents(
        db=None,
        workspace_name=workspace_name,
        observer=observer,
        observed=observed,
        query=query,
        top_k=limit,
        filters=filters,
        embedding=embedding,
    )

    return Representation.from_documents(documents)


async def get_observation_context(
    db: AsyncSession,
    workspace_name: str,
    session_name: str | None,
    message_ids: list[str],
    observer: str | None = None,
) -> list[models.Message]:
    """
    Retrieve messages for given message IDs along with surrounding context.

    Takes message IDs (from an observation's message_ids field) and retrieves those
    messages plus the messages immediately before and after each one to provide
    conversation context.

    Args:
        db: Database session
        workspace_name: Workspace identifier
        session_name: Session identifier (optional)
        message_ids: List of message IDs to retrieve
        observer: When provided and session_name is None, scope results
            to sessions this peer belongs to

    Returns:
        List of messages in chronological order, including the requested messages and surrounding context
    """
    if not message_ids:
        return []

    # Pre-fetch peer session scope if needed
    allowed_session_names: list[str] | None = None
    if observer and not session_name:
        from src.crud.message import get_peer_session_names

        allowed_session_names = await get_peer_session_names(
            db, workspace_name, observer
        )
        if not allowed_session_names:
            return []

    # Use a CTE to get seq_in_session values for target messages
    stmt = (
        select(models.Message.seq_in_session)
        .where(models.Message.workspace_name == workspace_name)
        .where(models.Message.public_id.in_(message_ids))
    )

    if session_name:
        stmt = stmt.where(models.Message.session_name == session_name)
    elif allowed_session_names is not None:
        stmt = stmt.where(models.Message.session_name.in_(allowed_session_names))

    target_seqs_cte = stmt.cte("target_seqs")

    # Query messages where seq_in_session is within ±1 of any target sequence
    # We use EXISTS with arithmetic to check if the message is adjacent to any target
    stmt = (
        select(models.Message)
        .where(models.Message.workspace_name == workspace_name)
        .where(
            select(target_seqs_cte.c.seq_in_session)
            .where(
                (
                    target_seqs_cte.c.seq_in_session - models.Message.seq_in_session
                ).between(-1, 1)
            )
            .exists()
        )
        .order_by(models.Message.seq_in_session.asc())
    )

    if session_name:
        stmt = stmt.where(models.Message.session_name == session_name)
    elif allowed_session_names is not None:
        stmt = stmt.where(models.Message.session_name.in_(allowed_session_names))

    result = await db.execute(stmt)
    messages = list(result.scalars().all())

    return messages


async def extract_preferences(
    workspace_name: str,
    session_name: str | None,
    observed: str,
    observer: str | None = None,
) -> dict[str, list[str]]:
    """
    Extract user preferences and standing instructions from conversation history.

    Uses semantic search to find messages that might contain preferences or instructions.
    This is language-agnostic and doesn't rely on keyword matching.

    Args:
        workspace_name: Workspace identifier
        session_name: Session identifier (optional)
        observed: The peer whose preferences to extract
        observer: When provided and session_name is None, scope results
            to sessions this peer belongs to

>nl -ba src/models.py | sed -n '1,430p'

Chunk ID: 108705
Wall time: 0.1629 seconds
Process exited with code 0
Original token count: 4662
Output:
     1	import datetime
     2	from logging import getLogger
     3	from typing import Any, final
     4	
     5	from dotenv import load_dotenv
     6	from nanoid import generate as generate_nanoid
     7	from pgvector.sqlalchemy import Vector
     8	from sqlalchemy import (
     9	    BigInteger,
    10	    Boolean,
    11	    CheckConstraint,
    12	    Column,
    13	    DateTime,
    14	    ForeignKey,
    15	    ForeignKeyConstraint,
    16	    Identity,
    17	    Index,
    18	    Integer,
    19	    Table,
    20	    UniqueConstraint,
    21	    text,
    22	)
    23	from sqlalchemy.dialects.postgresql import JSONB, TEXT
    24	from sqlalchemy.orm import Mapped, MappedColumn, mapped_column, relationship
    25	from sqlalchemy.sql import func
    26	from typing_extensions import override
    27	
    28	from src.config import settings
    29	from src.utils.types import DocumentLevel, TaskType, VectorSyncState
    30	
    31	from .db import Base
    32	
    33	load_dotenv(override=True)
    34	
    35	_VECTOR_DIM: int = settings.EMBEDDING.VECTOR_DIMENSIONS
    36	
    37	logger = getLogger(__name__)
    38	
    39	
    40	# Association table for many-to-many relationship between sessions and peers
    41	session_peers_table = Table(
    42	    "session_peers",
    43	    Base.metadata,
    44	    Column(
    45	        "workspace_name",
    46	        TEXT,
    47	        ForeignKey("workspaces.name"),
    48	        primary_key=[REDACTED]
    49	        nullable=False,
    50	    ),
    51	    Column(
    52	        "session_name",
    53	        TEXT,
    54	        primary_key=[REDACTED]
    55	        nullable=False,
    56	    ),
    57	    Column("peer_name", TEXT, primary_key=[REDACTED] nullable=False),
    58	    Column(
    59	        "configuration",
    60	        JSONB,
    61	        default=dict,
    62	        nullable=False,
    63	        server_default=text("'{}'::jsonb"),
    64	    ),
    65	    Column(
    66	        "internal_metadata",
    67	        JSONB,
    68	        default=dict,
    69	        nullable=False,
    70	        server_default=text("'{}'::jsonb"),
    71	    ),
    72	    Column(
    73	        "joined_at",
    74	        DateTime(timezone=True),
    75	        nullable=False,
    76	        server_default=func.now(),
    77	    ),
    78	    Column(
    79	        "left_at",
    80	        DateTime(timezone=True),
    81	        nullable=True,
    82	    ),
    83	    # Composite foreign key constraint for sessions
    84	    ForeignKeyConstraint(
    85	        ["session_name", "workspace_name"],
    86	        ["sessions.name", "sessions.workspace_name"],
    87	    ),
    88	    # Composite foreign key constraint for peers
    89	    ForeignKeyConstraint(
    90	        ["peer_name", "workspace_name"],
    91	        ["peers.name", "peers.workspace_name"],
    92	    ),
    93	)
    94	
    95	
    96	@final
    97	class Workspace(Base):
    98	    __tablename__: str = "workspaces"
    99	    id: Mapped[str] = mapped_column(TEXT, default=generate_nanoid, primary_key=[REDACTED]
   100	    name: Mapped[str] = mapped_column(TEXT, unique=True)
   101	    created_at: Mapped[datetime.datetime] = mapped_column(
   102	        DateTime(timezone=True), server_default=func.now(), index=True
   103	    )
   104	    h_metadata: Mapped[dict[str, Any]] = mapped_column(
   105	        "metadata", JSONB, default=dict, server_default=text("'{}'::jsonb")
   106	    )
   107	    internal_metadata: Mapped[dict[str, Any]] = mapped_column(
   108	        "internal_metadata", JSONB, default=dict, server_default=text("'{}'::jsonb")
   109	    )
   110	    configuration: Mapped[dict[str, Any]] = mapped_column(
   111	        JSONB, default=dict, server_default=text("'{}'::jsonb")
   112	    )
   113	
   114	    sessions = relationship(
   115	        "Session", back_populates="workspace", cascade="all, delete, delete-orphan"
   116	    )
   117	    peers = relationship(
   118	        "Peer", back_populates="workspace", cascade="all, delete, delete-orphan"
   119	    )
   120	    webhook_endpoints = relationship("WebhookEndpoint", back_populates="workspace")
   121	
   122	    __table_args__ = (
   123	        CheckConstraint("length(id) = 21", name="id_length"),
   124	        CheckConstraint("length(name) <= 512", name="name_length"),
   125	        CheckConstraint("id ~ '^[A-Za-z0-9_-]+$'", name="id_format"),
   126	    )
   127	
   128	
   129	@final
   130	class Peer(Base):
   131	    __tablename__: str = "peers"
   132	    id: Mapped[str] = mapped_column(TEXT, default=generate_nanoid, primary_key=[REDACTED]
   133	    name: Mapped[str] = mapped_column(TEXT, nullable=False)
   134	    h_metadata: Mapped[dict[str, Any]] = mapped_column(
   135	        "metadata", JSONB, default=dict, server_default=text("'{}'::jsonb")
   136	    )
   137	    internal_metadata: Mapped[dict[str, Any]] = mapped_column(
   138	        "internal_metadata", JSONB, default=dict, server_default=text("'{}'::jsonb")
   139	    )
   140	    created_at: Mapped[datetime.datetime] = mapped_column(
   141	        DateTime(timezone=True), server_default=func.now(), index=True
   142	    )
   143	    workspace_name: Mapped[str] = mapped_column(
   144	        ForeignKey("workspaces.name"), nullable=False, index=True
   145	    )
   146	    configuration: Mapped[dict[str, Any]] = mapped_column(
   147	        JSONB, default=dict, server_default=text("'{}'::jsonb")
   148	    )
   149	
   150	    workspace = relationship("Workspace", back_populates="peers")
   151	    sessions = relationship(
   152	        "Session", secondary=session_peers_table, back_populates="peers"
   153	    )
   154	
   155	    __table_args__ = (
   156	        UniqueConstraint("name", "workspace_name"),
   157	        CheckConstraint("length(id) = 21", name="id_length"),
   158	        CheckConstraint("length(name) <= 512", name="name_length"),
   159	        CheckConstraint("id ~ '^[A-Za-z0-9_-]+$'", name="id_format"),
   160	    )
   161	
   162	    def __repr__(self) -> str:
   163	        return f"Peer(id={self.id}, name={self.name}, workspace_name={self.workspace_name}, created_at={self.created_at}, h_metadata={self.h_metadata}, configuration={self.configuration})"
   164	
   165	
   166	@final
   167	class Session(Base):
   168	    __tablename__: str = "sessions"
   169	    id: Mapped[str] = mapped_column(TEXT, primary_key=[REDACTED] default=generate_nanoid)
   170	    name: Mapped[str] = mapped_column(TEXT)
   171	    is_active: Mapped[bool] = mapped_column(default=True, server_default=text("true"))
   172	    h_metadata: Mapped[dict[str, Any]] = mapped_column(
   173	        "metadata", JSONB, default=dict, server_default=text("'{}'::jsonb")
   174	    )
   175	    internal_metadata: Mapped[dict[str, Any]] = mapped_column(
   176	        "internal_metadata", JSONB, default=dict, server_default=text("'{}'::jsonb")
   177	    )
   178	    created_at: Mapped[datetime.datetime] = mapped_column(
   179	        DateTime(timezone=True), server_default=func.now(), index=True
   180	    )
   181	    workspace_name: Mapped[str] = mapped_column(
   182	        ForeignKey("workspaces.name"), nullable=False, index=True
   183	    )
   184	    configuration: Mapped[dict[str, Any]] = mapped_column(
   185	        JSONB, default=dict, server_default=text("'{}'::jsonb")
   186	    )
   187	
   188	    workspace = relationship("Workspace", back_populates="sessions")
   189	    peers = relationship(
   190	        "Peer", secondary=session_peers_table, back_populates="sessions"
   191	    )
   192	    messages = relationship("Message", back_populates="session")
   193	
   194	    __table_args__ = (
   195	        UniqueConstraint("name", "workspace_name"),
   196	        CheckConstraint("length(name) <= 512", name="name_length"),
   197	        CheckConstraint("length(id) = 21", name="id_length"),
   198	        CheckConstraint("id ~ '^[A-Za-z0-9_-]+$'", name="id_format"),
   199	    )
   200	
   201	    def __repr__(self) -> str:
   202	        return f"Session(id={self.id}, name={self.name}, workspace_name={self.workspace_name}, is_active={self.is_active}, created_at={self.created_at}, h_metadata={self.h_metadata})"
   203	
   204	
   205	@final
   206	class Message(Base):
   207	    __tablename__: str = "messages"
   208	    id: Mapped[int] = mapped_column(
   209	        BigInteger, Identity(), primary_key=[REDACTED] autoincrement=True
   210	    )
   211	    public_id: Mapped[str] = mapped_column(
   212	        TEXT,
   213	        unique=True,
   214	        default=generate_nanoid,
   215	    )
   216	    # NOTE: Messages in Honcho 2.0 could historically be stored outside of a session.
   217	    # We have since assigned all of these messages to a default session.
   218	    session_name: Mapped[str] = mapped_column(TEXT, nullable=False)
   219	    content: Mapped[str] = mapped_column(TEXT)
   220	    h_metadata: Mapped[dict[str, Any]] = mapped_column(
   221	        "metadata", JSONB, default=dict, server_default=text("'{}'::jsonb")
   222	    )
   223	    internal_metadata: Mapped[dict[str, Any]] = mapped_column(
   224	        "internal_metadata", JSONB, default=dict, server_default=text("'{}'::jsonb")
   225	    )
   226	    token_count: Mapped[int] = mapped_column(Integer, default=0, nullable=False)
   227	    seq_in_session: Mapped[int] = mapped_column(BigInteger, nullable=False)
   228	
   229	    created_at: Mapped[datetime.datetime] = mapped_column(
   230	        DateTime(timezone=True), server_default=func.now(), index=True
   231	    )
   232	    # Note: Foreign key relationships established via composite ForeignKeyConstraint below
   233	    peer_name: Mapped[str] = mapped_column(TEXT, index=True)
   234	    workspace_name: Mapped[str] = mapped_column(TEXT, index=True)
   235	
   236	    session = relationship("Session", back_populates="messages")
   237	
   238	    __table_args__ = (
   239	        CheckConstraint("length(public_id) = 21", name="public_id_length"),
   240	        CheckConstraint("public_id ~ '^[A-Za-z0-9_-]+$'", name="public_id_format"),
   241	        CheckConstraint("length(content) <= 65535", name="content_length"),
   242	        # Composite foreign key constraint for sessions
   243	        ForeignKeyConstraint(
   244	            ["session_name", "workspace_name"],
   245	            ["sessions.name", "sessions.workspace_name"],
   246	        ),
   247	        # Composite foreign key constraint for peers
   248	        ForeignKeyConstraint(
   249	            ["peer_name", "workspace_name"],
   250	            ["peers.name", "peers.workspace_name"],
   251	        ),
   252	        Index(
   253	            "ix_messages_session_lookup",
   254	            "session_name",
   255	            "id",
   256	            postgresql_include=["id", "created_at"],
   257	        ),
   258	        UniqueConstraint(
   259	            "workspace_name",
   260	            "session_name",
   261	            "seq_in_session",
   262	        ),
   263	        # Full text search index on content column
   264	        Index(
   265	            "ix_messages_content_gin",
   266	            text("to_tsvector('english', content)"),
   267	            postgresql_using="gin",
   268	        ),
   269	    )
   270	
   271	    @override
   272	    def __repr__(self) -> str:
   273	        return f"Message(id={self.id}, session_name={self.session_name}, peer_name={self.peer_name}, content={self.content})"
   274	
   275	
   276	@final
   277	class MessageEmbedding(Base):
   278	    __tablename__: str = "message_embeddings"
   279	
   280	    id: Mapped[int] = mapped_column(
   281	        BigInteger, Identity(), primary_key=[REDACTED] autoincrement=True
   282	    )
   283	    content: Mapped[str] = mapped_column(TEXT)
   284	    embedding: MappedColumn[Any] = mapped_column(Vector(_VECTOR_DIM), nullable=True)
   285	    message_id: Mapped[str] = mapped_column(
   286	        ForeignKey("messages.public_id", ondelete="CASCADE"), nullable=False, index=True
   287	    )
   288	    workspace_name: Mapped[str] = mapped_column(
   289	        ForeignKey("workspaces.name"), nullable=False, index=True
   290	    )
   291	    session_name: Mapped[str] = mapped_column(TEXT, nullable=False, index=True)
   292	    peer_name: Mapped[str] = mapped_column(TEXT, nullable=False, index=True)
   293	    created_at: Mapped[datetime.datetime] = mapped_column(
   294	        DateTime(timezone=True), server_default=func.now(), index=True
   295	    )
   296	    # Vector sync state tracking
   297	    sync_state: Mapped[VectorSyncState] = mapped_column(
   298	        TEXT, nullable=False, server_default="pending", index=True
   299	    )
   300	    last_sync_at: Mapped[datetime.datetime | None] = mapped_column(
   301	        DateTime(timezone=True), nullable=True
   302	    )
   303	    sync_attempts: Mapped[int] = mapped_column(
   304	        Integer, nullable=False, default=0, server_default=text("0")
   305	    )
   306	
   307	    __table_args__ = (
   308	        # Compound foreign key constraints
   309	        ForeignKeyConstraint(
   310	            ["session_name", "workspace_name"],
   311	            ["sessions.name", "sessions.workspace_name"],
   312	        ),
   313	        ForeignKeyConstraint(
   314	            ["peer_name", "workspace_name"],
   315	            ["peers.name", "peers.workspace_name"],
   316	        ),
   317	        # HNSW index on embedding column for efficient similarity search
   318	        Index(
   319	            "ix_message_embeddings_embedding_hnsw",
   320	            "embedding",
   321	            postgresql_using="hnsw",
   322	            postgresql_with={"m": 16, "ef_construction": 64},
   323	            postgresql_ops={"embedding": "vector_cosine_ops"},
   324	        ),
   325	        # Composite index for efficient reconciliation queries
   326	        Index(
   327	            "ix_message_embeddings_sync_state_last_sync_at",
   328	            "sync_state",
   329	            "last_sync_at",
   330	        ),
   331	    )
   332	
   333	
   334	@final
   335	class Collection(Base):
   336	    __tablename__: str = "collections"
   337	
   338	    id: Mapped[str] = mapped_column(TEXT, default=generate_nanoid, primary_key=[REDACTED]
   339	    observer: Mapped[str] = mapped_column(TEXT, index=True)
   340	    observed: Mapped[str] = mapped_column(TEXT, index=True)
   341	    created_at: Mapped[datetime.datetime] = mapped_column(
   342	        DateTime(timezone=True), server_default=func.now(), index=True
   343	    )
   344	    h_metadata: Mapped[dict[str, Any]] = mapped_column(
   345	        "metadata", JSONB, default=dict, server_default=text("'{}'::jsonb")
   346	    )
   347	    internal_metadata: Mapped[dict[str, Any]] = mapped_column(
   348	        "internal_metadata", JSONB, default=dict, server_default=text("'{}'::jsonb")
   349	    )
   350	    documents = relationship(
   351	        "Document", back_populates="collection", cascade="all, delete, delete-orphan"
   352	    )
   353	    workspace_name: Mapped[str] = mapped_column(
   354	        ForeignKey("workspaces.name"), nullable=False, index=True
   355	    )
   356	
   357	    __table_args__ = (
   358	        UniqueConstraint(
   359	            "observer",
   360	            "observed",
   361	            "workspace_name",
   362	        ),
   363	        CheckConstraint("length(id) = 21", name="id_length"),
   364	        CheckConstraint("id ~ '^[A-Za-z0-9_-]+$'", name="id_format"),
   365	        # Composite foreign key constraint for observer peer
   366	        ForeignKeyConstraint(
   367	            ["observer", "workspace_name"],
   368	            ["peers.name", "peers.workspace_name"],
   369	        ),
   370	        # Composite foreign key constraint for observed peer
   371	        ForeignKeyConstraint(
   372	            ["observed", "workspace_name"],
   373	            ["peers.name", "peers.workspace_name"],
   374	        ),
   375	    )
   376	
   377	
   378	@final
   379	class Document(Base):
   380	    __tablename__: str = "documents"
   381	    id: Mapped[str] = mapped_column(TEXT, default=generate_nanoid, primary_key=[REDACTED]
   382	    internal_metadata: Mapped[dict[str, Any]] = mapped_column(
   383	        "internal_metadata", JSONB, default=dict, server_default=text("'{}'::jsonb")
   384	    )
   385	    content: Mapped[str] = mapped_column(TEXT)
   386	    level: Mapped[DocumentLevel] = mapped_column(
   387	        TEXT, nullable=False, server_default="explicit"
   388	    )
   389	    times_derived: Mapped[int] = mapped_column(
   390	        Integer, nullable=False, server_default=text("1")
   391	    )
   392	    embedding: MappedColumn[Any] = mapped_column(Vector(_VECTOR_DIM), nullable=True)
   393	    source_ids: Mapped[list[str] | None] = mapped_column(
   394	        JSONB, nullable=True, server_default=text("NULL")
   395	    )
   396	    created_at: Mapped[datetime.datetime] = mapped_column(
   397	        DateTime(timezone=True), server_default=func.now(), index=True
   398	    )
   399	
   400	    observer: Mapped[str] = mapped_column(TEXT, index=True)
   401	    observed: Mapped[str] = mapped_column(TEXT, index=True)
   402	    workspace_name: Mapped[str] = mapped_column(
   403	        ForeignKey("workspaces.name"), nullable=False, index=True
   404	    )
   405	    session_name: Mapped[str | None] = mapped_column(TEXT, nullable=True, index=True)
   406	    deleted_at: Mapped[datetime.datetime | None] = mapped_column(
   407	        DateTime(timezone=True), nullable=True, index=True, default=None
   408	    )
   409	
   410	    # Vector sync state tracking
   411	    sync_state: Mapped[VectorSyncState] = mapped_column(
   412	        TEXT, nullable=False, server_default="pending", index=True
   413	    )
   414	    last_sync_at: Mapped[datetime.datetime | None] = mapped_column(
   415	        DateTime(timezone=True), nullable=True
   416	    )
   417	    sync_attempts: Mapped[int] = mapped_column(
   418	        Integer, nullable=False, default=0, server_default=text("0")
   419	    )
   420	
   421	    collection = relationship("Collection", back_populates="documents")
   422	
   423	    __table_args__ = (
   424	        CheckConstraint("length(id) = 21", name="id_length"),
   425	        CheckConstraint("length(content) <= 65535", name="content_length"),
   426	        CheckConstraint("id ~ '^[A-Za-z0-9_-]+$'", name="id_format"),
   427	        # Composite foreign key constraint for collections
   428	        ForeignKeyConstraint(
   429	            ["observer", "observed", "workspace_name"],
   430	            [

>nl -ba src/deriver/enqueue.py | sed -n '200,390p'

Chunk ID: f6f36b
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1894
Output:
   200	        task_type="representation",
   201	        observers=observers,
   202	        observed=observed,
   203	    )
   204	    return {
   205	        "work_unit_key": construct_work_unit_key(workspace_name, processed_payload),
   206	        "payload": processed_payload,
   207	        "session_id": session_id,
   208	        "task_type": "representation",
   209	        "workspace_name": workspace_name,
   210	        "message_id": message_id,
   211	    }
   212	
   213	
   214	def create_summary_record(
   215	    message: dict[str, Any],
   216	    configuration: ResolvedConfiguration,
   217	    session_id: str,
   218	    message_seq_in_session: int,
   219	) -> dict[str, Any]:
   220	    """
   221	    Create a queue record for summary task.
   222	
   223	    Args:
   224	        message: The message payload
   225	        session_id: Session ID
   226	        message_seq_in_session: The sequence number of the message in the session
   227	
   228	    Returns:
   229	        Queue record dictionary with workspace_name and message_id as separate fields
   230	    """
   231	    workspace_name = message.get("workspace_name")
   232	    message_id = message.get("message_id")
   233	
   234	    if not isinstance(workspace_name, str):
   235	        raise ValueError("workspace_name is required and must be a string")
   236	    if not isinstance(message_id, int):
   237	        raise ValueError("message_id is required and must be an integer")
   238	
   239	    processed_payload = create_payload(
   240	        message=message,
   241	        configuration=configuration,
   242	        task_type="summary",
   243	        message_seq_in_session=message_seq_in_session,
   244	    )
   245	    return {
   246	        "work_unit_key": construct_work_unit_key(workspace_name, processed_payload),
   247	        "payload": processed_payload,
   248	        "session_id": session_id,
   249	        "task_type": "summary",
   250	        "workspace_name": workspace_name,
   251	        "message_id": message_id,
   252	    }
   253	
   254	
   255	def get_effective_observe_me(
   256	    observed: str, peers_with_configuration: dict[str, list[dict[str, Any]]]
   257	) -> bool:
   258	    """
   259	    Determine the effective observe_me setting for a sender, considering session and peer configurations.
   260	
   261	    Args:
   262	        observed: Name of the sender
   263	        peers_with_configuration: Dictionary of peer configurations
   264	
   265	    Returns:
   266	        True if observe_me is enabled, False otherwise
   267	    """
   268	    # If the sender is not in peers_with_configuration, they left after sending a message.
   269	    # We'll use the default behavior of observing the sender by instantiating the default
   270	    # peer-level and session-level configs.
   271	    configuration: list[Any] = peers_with_configuration.get(observed, [{}, {}])
   272	    sender_session_peer_config = (
   273	        schemas.SessionPeerConfig(**configuration[1]) if configuration[1] else None
   274	    )
   275	    sender_peer_config = (
   276	        schemas.PeerConfig(**configuration[0])
   277	        if configuration[0]
   278	        else schemas.PeerConfig()
   279	    )
   280	
   281	    # Session peer config takes precedence if it exists and has observe_me set
   282	    if sender_session_peer_config and sender_session_peer_config.observe_me is not None:
   283	        return sender_session_peer_config.observe_me
   284	
   285	    # Otherwise use peer config
   286	    return (
   287	        sender_peer_config.observe_me
   288	        if sender_peer_config.observe_me is not None
   289	        else True
   290	    )
   291	
   292	
   293	async def generate_queue_records(
   294	    db_session: AsyncSession,
   295	    message: dict[str, Any],
   296	    peers_with_configuration: dict[str, list[dict[str, Any]]],
   297	    session_id: str,
   298	    conf: ResolvedConfiguration,
   299	) -> list[dict[str, Any]]:
   300	    """
   301	    Process a single message and generate queue records based on configurations.
   302	
   303	    Args:
   304	        db_session: The database session
   305	        message: The message payload
   306	        peers_with_configuration: Dictionary of peer configurations
   307	        session_id: Session ID
   308	        configuration: Resolved configuration for this particular message
   309	
   310	    Returns:
   311	        List of queue records for this message
   312	    """
   313	    observed = message["peer_name"]
   314	    message_id: int = message["message_id"]
   315	
   316	    # Prefer the sequence captured during message creation; fallback only if missing
   317	    message_seq_in_session = int(message.get("seq_in_session") or 0)
   318	    if message_seq_in_session <= 0:
   319	        message_seq_in_session = await crud.get_message_seq_in_session(
   320	            db_session,
   321	            workspace_name=message["workspace_name"],
   322	            session_name=message["session_name"],
   323	            message_id=message_id,
   324	        )
   325	
   326	    records: list[dict[str, Any]] = []
   327	
   328	    if conf.summary.enabled and (
   329	        message_seq_in_session % conf.summary.messages_per_short_summary == 0
   330	        or message_seq_in_session % conf.summary.messages_per_long_summary == 0
   331	    ):
   332	        records.append(
   333	            create_summary_record(
   334	                message,
   335	                configuration=conf,
   336	                session_id=session_id,
   337	                message_seq_in_session=message_seq_in_session,
   338	            )
   339	        )
   340	
   341	    # Check if the sender should be observed based on peer configuration
   342	    should_observe = get_effective_observe_me(observed, peers_with_configuration)
   343	
   344	    if not conf.reasoning.enabled:
   345	        return records
   346	
   347	    # Collect all observers into a single list
   348	    observers: list[str] = []
   349	
   350	    if should_observe:
   351	        # Self-observation: the sender observes themselves
   352	        observers.append(observed)
   353	
   354	        # Other peers who want to observe
   355	        for peer_name, peer_conf in peers_with_configuration.items():
   356	            if peer_name == observed:
   357	                continue
   358	
   359	            # If the observer peer has left the session, skip them
   360	            if not peer_conf[2]:
   361	                continue
   362	
   363	            session_peer_config = (
   364	                schemas.SessionPeerConfig(**peer_conf[1]) if peer_conf[1] else None
   365	            )
   366	
   367	            if session_peer_config is None or not session_peer_config.observe_others:
   368	                continue
   369	
   370	            observers.append(peer_name)
   371	
   372	    # Create a single record with all observers (if any)
   373	    if observers:
   374	        records.append(
   375	            create_representation_record(
   376	                message,
   377	                conf,
   378	                observed=observed,
   379	                observers=observers,
   380	                session_id=session_id,
   381	            )
   382	        )
   383	
   384	    logger.debug(
   385	        "message %s from %s created %s queue items with %s observers",
   386	        message_id,
   387	        observed,
   388	        len(records),
   389	        len(observers),
   390	    )

>nl -ba src/deriver/deriver.py | sed -n '1,210p'

Chunk ID: 6751f6
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2172
Output:
     1	import logging
     2	import time
     3	
     4	from src import crud
     5	from src.config import ConfiguredModelSettings, settings
     6	from src.crud.representation import RepresentationManager
     7	from src.dependencies import tracked_db
     8	from src.llm import honcho_llm_call
     9	from src.models import Message
    10	from src.schemas import ResolvedConfiguration
    11	from src.telemetry import prometheus_metrics
    12	from src.telemetry.events import RepresentationCompletedEvent, emit
    13	from src.telemetry.logging import accumulate_metric, log_performance_metrics
    14	from src.telemetry.prometheus.metrics import (
    15	    DeriverComponents,
    16	    DeriverTaskTypes,
    17	    TokenTypes,
    18	)
    19	from src.telemetry.sentry import with_sentry_transaction
    20	from src.utils.config_helpers import get_configuration
    21	from src.utils.formatting import format_new_turn_with_timestamp
    22	from src.utils.representation import PromptRepresentation, Representation
    23	from src.utils.tokens import track_deriver_input_tokens
    24	
    25	from .prompts import estimate_deriver_prompt_tokens, minimal_deriver_prompt
    26	
    27	logger = logging.getLogger(__name__)
    28	
    29	
    30	def _get_deriver_model_config() -> ConfiguredModelSettings:
    31	    return settings.DERIVER.MODEL_CONFIG
    32	
    33	
    34	@with_sentry_transaction("minimal_deriver_batch", op="deriver")
    35	async def process_representation_tasks_batch(
    36	    messages: list[Message],
    37	    message_level_configuration: ResolvedConfiguration | None,
    38	    *,
    39	    observers: list[str],
    40	    observed: str,
    41	    queue_item_message_ids: list[int],
    42	) -> None:
    43	    """
    44	    Process messages with minimal overhead - single LLM call, save to multiple collections.
    45	
    46	    Args:
    47	        messages: List of messages to process (includes interleaving context).
    48	        message_level_configuration: Optional configuration override.
    49	        observers: List of observer peer IDs (collections to save to).
    50	        observed: The observed peer ID.
    51	        queue_item_message_ids: Message IDs from queue items being processed
    52	    """
    53	    if not messages:
    54	        return
    55	
    56	    overall_start = time.perf_counter()
    57	
    58	    messages.sort(key=lambda x: x.id)
    59	    latest_message = messages[-1]
    60	    earliest_message = messages[0]
    61	
    62	    # Get configuration if not provided
    63	    # TODO: this appears to be a very rare edge case coming out of `get_queue_item_batch` in queue_manager.py,
    64	    # possible that we can remove this and require configuration to come through with the payload.
    65	    if message_level_configuration is None:
    66	        async with tracked_db("minimal_deriver.get_config") as db:
    67	            message_level_configuration = get_configuration(
    68	                None,
    69	                await crud.get_session(
    70	                    db, latest_message.session_name, latest_message.workspace_name
    71	                ),
    72	                await crud.get_workspace(
    73	                    db, workspace_name=latest_message.workspace_name
    74	                ),
    75	            )
    76	
    77	    # Skip if disabled
    78	    if message_level_configuration.reasoning.enabled is False:
    79	        return
    80	
    81	    custom_instructions = message_level_configuration.reasoning.custom_instructions
    82	
    83	    accumulate_metric(
    84	        f"minimal_deriver_{latest_message.id}_{observed}",
    85	        "starting_message_id",
    86	        earliest_message.id,
    87	        "id",
    88	    )
    89	    accumulate_metric(
    90	        f"minimal_deriver_{latest_message.id}_{observed}",
    91	        "ending_message_id",
    92	        latest_message.id,
    93	        "id",
    94	    )
    95	
    96	    # Format messages with timestamps
    97	    formatted_messages = "\n".join(
    98	        format_new_turn_with_timestamp(msg.content, msg.created_at, msg.peer_name)
    99	        for msg in messages
   100	    )
   101	
   102	    # Track token usage - count only tokens from messages being processed
   103	    prompt_tokens = estimate_deriver_prompt_tokens(custom_instructions)
   104	    queue_item_message_ids_set = set(queue_item_message_ids)
   105	    messages_tokens = sum(
   106	        msg.token_count for msg in messages if msg.id in queue_item_message_ids_set
   107	    )
   108	    track_deriver_input_tokens(
   109	        task_type=DeriverTaskTypes.INGESTION,
   110	        components={
   111	            DeriverComponents.PROMPT: prompt_tokens,
   112	            DeriverComponents.MESSAGES: messages_tokens,
   113	        },
   114	    )
   115	
   116	    # Build prompt
   117	    prompt = minimal_deriver_prompt(
   118	        peer_id=observed,
   119	        messages=formatted_messages,
   120	        custom_instructions=custom_instructions,
   121	    )
   122	
   123	    context_prep_duration = (time.perf_counter() - overall_start) * 1000
   124	    accumulate_metric(
   125	        f"minimal_deriver_{latest_message.id}_{observed}",
   126	        "context_preparation",
   127	        context_prep_duration,
   128	        "ms",
   129	    )
   130	
   131	    # validation on settings means max_tokens will always be > 0
   132	    base_model_config = _get_deriver_model_config()
   133	    max_tokens = base_model_config.max_output_tokens or settings.LLM.DEFAULT_MAX_TOKENS
   134	    model_config = base_model_config
   135	
   136	    # Single LLM call
   137	    llm_start = time.perf_counter()
   138	    response = await honcho_llm_call(
   139	        model_config=model_config,
   140	        prompt=prompt,
   141	        max_tokens=[REDACTED]
   142	        track_name="Minimal Deriver",
   143	        response_model=PromptRepresentation,
   144	        json_mode=True,
   145	        max_input_tokens=[REDACTED]
   146	        enable_retry=True,
   147	        retry_attempts=3,
   148	        trace_name="minimal_deriver",
   149	    )
   150	    llm_duration = (time.perf_counter() - llm_start) * 1000
   151	
   152	    accumulate_metric(
   153	        f"minimal_deriver_{latest_message.id}_{observed}",
   154	        "llm_call_duration",
   155	        llm_duration,
   156	        "ms",
   157	    )
   158	
   159	    # Prometheus metrics
   160	    if settings.METRICS.ENABLED:
   161	        prometheus_metrics.record_deriver_tokens(
   162	            count=response.output_tokens,
   163	            task_type=DeriverTaskTypes.INGESTION.value,
   164	            token_type=[REDACTED]
   165	            component=DeriverComponents.OUTPUT_TOTAL.value,
   166	        )
   167	
   168	    message_ids = [m.id for m in messages if m.peer_name == observed]
   169	
   170	    # Convert to Representation and save
   171	    observations = Representation.from_prompt_representation(
   172	        response.content,
   173	        message_ids,
   174	        latest_message.session_name,
   175	        latest_message.created_at,
   176	    )
   177	
   178	    if observations.is_empty() or not message_ids:
   179	        logger.warning(
   180	            "Deriver generated zero observations for messages %s:%s in %s/%s!",
   181	            earliest_message.id,
   182	            latest_message.id,
   183	            latest_message.workspace_name,
   184	            latest_message.session_name,
   185	        )
   186	    else:
   187	        # Save to all observer collections
   188	        for observer in observers:
   189	            representation_manager = RepresentationManager(
   190	                workspace_name=latest_message.workspace_name,
   191	                observer=observer,
   192	                observed=observed,
   193	            )
   194	
   195	            try:
   196	                await representation_manager.save_representation(
   197	                    observations,
   198	                    message_ids,
   199	                    latest_message.session_name,
   200	                    latest_message.created_at,
   201	                    message_level_configuration,
   202	                )
   203	            except Exception as e:
   204	                logger.error(
   205	                    "Failed to save representation for observer %s: %s", observer, e
   206	                )
   207	
   208	    # Log metrics
   209	    overall_duration = (time.perf_counter() - overall_start) * 1000
   210	    accumulate_metric(

>nl -ba src/utils/representation.py | sed -n '1,360p'

Chunk ID: b2643d
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 4258
Output:
     1	from collections.abc import Sequence
     2	from datetime import datetime
     3	from typing import Any
     4	
     5	from pydantic import BaseModel, Field, field_validator
     6	
     7	from src import models
     8	from src.utils.formatting import parse_datetime_iso
     9	
    10	
    11	def _strip_microseconds_and_timezone(timestamp: datetime) -> datetime:
    12	    """
    13	    Remove microseconds and timezone info from a datetime for stable string formatting.
    14	    """
    15	    return timestamp.replace(microsecond=0, tzinfo=None)
    16	
    17	
    18	def flatten_message_ids(
    19	    message_ids: list[int] | list[list[int]] | list[tuple[int, int]],
    20	) -> list[int]:
    21	    """
    22	    Flatten message_ids that may be in old tuple format or nested list format.
    23	
    24	    This handles backwards compatibility with the old schema where message_ids
    25	    was list[tuple[int, int]] representing ranges, and the new schema where
    26	    it's list[int] representing individual message IDs.
    27	
    28	    Args:
    29	        message_ids: Either a flat list of ints, nested list, or list of tuples
    30	
    31	    Returns:
    32	        A flat list of unique message IDs, sorted
    33	
    34	    Examples:
    35	        [1, 2, 3] -> [1, 2, 3]
    36	        [[1, 2], [3, 4]] -> [1, 2, 3, 4]
    37	        [(105, 105)] -> [105]
    38	        [[105, 105]] -> [105]
    39	    """
    40	    result: list[int] = []
    41	    for item in message_ids:
    42	        if isinstance(item, (list | tuple)):
    43	            # Nested list or tuple - flatten it
    44	            result.extend(item)
    45	        else:
    46	            # Already flat
    47	            result.append(item)
    48	    # Remove duplicates and sort
    49	    return sorted(set(result))
    50	
    51	
    52	class ObservationMetadata(BaseModel):
    53	    id: str = Field(default="", description="Document ID for this observation")
    54	    created_at: datetime
    55	    message_ids: list[int]
    56	    session_name: str | None = None
    57	
    58	
    59	class ExplicitObservationBase(BaseModel):
    60	    content: str = Field(description="The explicit observation")
    61	
    62	
    63	class DeductiveObservationBase(BaseModel):
    64	    source_ids: list[str] = Field(
    65	        description="Document IDs of premise observations for tree traversal",
    66	        default_factory=list,
    67	    )
    68	    premises: list[str] = Field(
    69	        description="Human-readable premise text for display",
    70	        default_factory=list,
    71	    )
    72	    conclusion: str = Field(description="The deductive conclusion")
    73	
    74	
    75	class InductiveObservationBase(BaseModel):
    76	    """Base model for inductive observations - patterns, generalizations, and personality insights."""
    77	
    78	    source_ids: list[str] = Field(
    79	        description="Document IDs of source observations for tree traversal",
    80	        default_factory=list,
    81	    )
    82	    sources: list[str] = Field(
    83	        description="Human-readable source text for display",
    84	        default_factory=list,
    85	    )
    86	    pattern_type: str = Field(
    87	        description="Type of pattern: 'preference', 'behavior', 'personality', 'tendency', 'correlation'",
    88	        default="pattern",
    89	    )
    90	    conclusion: str = Field(description="The inductive generalization or pattern")
    91	    confidence: str = Field(
    92	        description="Confidence level: 'high', 'medium', 'low'",
    93	        default="medium",
    94	    )
    95	
    96	
    97	class ContradictionObservationBase(BaseModel):
    98	    """Base model for contradiction observations - when user has made conflicting statements."""
    99	
   100	    source_ids: list[str] = Field(
   101	        description="Document IDs of the contradicting observations",
   102	        default_factory=list,
   103	    )
   104	    sources: list[str] = Field(
   105	        description="Human-readable text of the contradicting statements",
   106	        default_factory=list,
   107	    )
   108	    content: str = Field(description="Description of the contradiction")
   109	
   110	
   111	class PromptRepresentation(BaseModel):
   112	    """
   113	    The representation format that is used when getting structured output from an LLM.
   114	    """
   115	
   116	    explicit: list[ExplicitObservationBase] = Field(
   117	        description="Facts LITERALLY stated by the user - direct quotes or clear paraphrases only, no interpretation or inference. Example: ['The user is 25 years old', 'The user has a dog named Rover']",
   118	        default_factory=list,
   119	    )
   120	
   121	    @field_validator("explicit", mode="before")
   122	    @classmethod
   123	    def convert_none_to_empty_list(cls, v: Any) -> Any:
   124	        """Convert None to empty list - handles LLMs returning null instead of []."""
   125	        if v is None:
   126	            return []
   127	        return v
   128	
   129	
   130	class ExplicitObservation(ExplicitObservationBase, ObservationMetadata):
   131	    """Explicit observation with content and metadata."""
   132	
   133	    def __str__(self) -> str:
   134	        return f"[{_strip_microseconds_and_timezone(self.created_at)}] {self.content}"
   135	
   136	    def str_with_id(self) -> str:
   137	        """Format with ID prefix for use by agents that need to reference observations."""
   138	        id_prefix = f"[id:{self.id}] " if self.id else ""
   139	        return f"{id_prefix}[{_strip_microseconds_and_timezone(self.created_at)}] {self.content}"
   140	
   141	    def __hash__(self) -> int:
   142	        """
   143	        Make ExplicitObservation hashable for use in sets.
   144	        """
   145	        return hash((self.content, self.created_at, self.session_name))
   146	
   147	    def __eq__(self, other: object) -> bool:
   148	        """
   149	        Define equality for ExplicitObservation objects.
   150	        Two observations are equal if all their fields match.
   151	        """
   152	        if not isinstance(other, ExplicitObservation):
   153	            return False
   154	        return (
   155	            self.content == other.content
   156	            and self.created_at == other.created_at
   157	            and self.session_name == other.session_name
   158	        )
   159	
   160	
   161	class DeductiveObservation(DeductiveObservationBase, ObservationMetadata):
   162	    """Deductive observation with multiple premises and one conclusion, plus metadata."""
   163	
   164	    def __str__(self) -> str:
   165	        premises_text = "\n".join(f"    - {premise}" for premise in self.premises)
   166	        return f"[{_strip_microseconds_and_timezone(self.created_at)}] {self.conclusion}\n{premises_text}"
   167	
   168	    def str_with_id(self) -> str:
   169	        """Format with ID prefix for use by agents that need to reference observations."""
   170	        id_prefix = f"[id:{self.id}] " if self.id else ""
   171	        premises_text = "\n".join(f"    - {premise}" for premise in self.premises)
   172	        return f"{id_prefix}[{_strip_microseconds_and_timezone(self.created_at)}] {self.conclusion}\n{premises_text}"
   173	
   174	    def str_no_timestamps(self) -> str:
   175	        premises_text = "\n".join(f"    - {premise}" for premise in self.premises)
   176	        return f"{self.conclusion}\n{premises_text}"
   177	
   178	    def __hash__(self) -> int:
   179	        """
   180	        Make DeductiveObservation hashable for use in sets. NOTE: premises are not included in the hash.
   181	        """
   182	        return hash((self.conclusion, self.created_at, self.session_name))
   183	
   184	    def __eq__(self, other: object) -> bool:
   185	        """
   186	        Define equality for DeductiveObservation objects.
   187	        Two observations are equal if all their fields match -- NOTE: premises are not included in the equality check.
   188	        """
   189	        if not isinstance(other, DeductiveObservation):
   190	            return False
   191	        return (
   192	            self.conclusion == other.conclusion
   193	            and self.created_at == other.created_at
   194	            and self.session_name == other.session_name
   195	        )
   196	
   197	
   198	class InductiveObservation(InductiveObservationBase, ObservationMetadata):
   199	    """Inductive observation with sources, pattern type, and confidence, plus metadata."""
   200	
   201	    def __str__(self) -> str:
   202	        sources_text = ""
   203	        if self.sources:
   204	            source_lines = [f"    - {source}" for source in self.sources]
   205	            sources_text = "\n" + "\n".join(source_lines)
   206	        return f"[{_strip_microseconds_and_timezone(self.created_at)}] [{self.confidence}] {self.conclusion}{sources_text}"
   207	
   208	    def str_with_id(self) -> str:
   209	        """Format with ID prefix for use by agents that need to reference observations."""
   210	        id_prefix = f"[id:{self.id}] " if self.id else ""
   211	        sources_text = ""
   212	        if self.sources:
   213	            source_lines = [f"    - {source}" for source in self.sources]
   214	            sources_text = "\n" + "\n".join(source_lines)
   215	        return f"{id_prefix}[{_strip_microseconds_and_timezone(self.created_at)}] [{self.confidence}] {self.conclusion}{sources_text}"
   216	
   217	    def str_no_timestamps(self) -> str:
   218	        sources_text = ""
   219	        if self.sources:
   220	            source_lines = [f"    - {source}" for source in self.sources]
   221	            sources_text = "\n" + "\n".join(source_lines)
   222	        return f"[{self.confidence}] {self.conclusion}{sources_text}"
   223	
   224	    def __hash__(self) -> int:
   225	        """Make InductiveObservation hashable for use in sets."""
   226	        return hash((self.conclusion, self.created_at, self.session_name))
   227	
   228	    def __eq__(self, other: object) -> bool:
   229	        """Define equality for InductiveObservation objects."""
   230	        if not isinstance(other, InductiveObservation):
   231	            return False
   232	        return (
   233	            self.conclusion == other.conclusion
   234	            and self.created_at == other.created_at
   235	            and self.session_name == other.session_name
   236	        )
   237	
   238	
   239	class ContradictionObservation(ContradictionObservationBase, ObservationMetadata):
   240	    """Contradiction observation - notes when user has made conflicting statements, plus metadata."""
   241	
   242	    def __str__(self) -> str:
   243	        sources_text = ""
   244	        if self.sources:
   245	            source_lines = [f"    - {source}" for source in self.sources]
   246	            sources_text = "\n" + "\n".join(source_lines)
   247	        return f"[{_strip_microseconds_and_timezone(self.created_at)}] CONTRADICTION: {self.content}{sources_text}"
   248	
   249	    def str_with_id(self) -> str:
   250	        """Format with ID prefix for use by agents that need to reference observations."""
   251	        id_prefix = f"[id:{self.id}] " if self.id else ""
   252	        sources_text = ""
   253	        if self.sources:
   254	            source_lines = [f"    - {source}" for source in self.sources]
   255	            sources_text = "\n" + "\n".join(source_lines)
   256	        return f"{id_prefix}[{_strip_microseconds_and_timezone(self.created_at)}] CONTRADICTION: {self.content}{sources_text}"
   257	
   258	    def str_no_timestamps(self) -> str:
   259	        sources_text = ""
   260	        if self.sources:
   261	            source_lines = [f"    - {source}" for source in self.sources]
   262	            sources_text = "\n" + "\n".join(source_lines)
   263	        return f"CONTRADICTION: {self.content}{sources_text}"
   264	
   265	    def __hash__(self) -> int:
   266	        """Make ContradictionObservation hashable for use in sets."""
   267	        return hash((self.content, self.created_at, self.session_name))
   268	
   269	    def __eq__(self, other: object) -> bool:
   270	        """Define equality for ContradictionObservation objects."""
   271	        if not isinstance(other, ContradictionObservation):
   272	            return False
   273	        return (
   274	            self.content == other.content
   275	            and self.created_at == other.created_at
   276	            and self.session_name == other.session_name
   277	        )
   278	
   279	
   280	class Representation(BaseModel):
   281	    """
   282	    A Representation is a traversable and diffable map of observations.
   283	    At the base, we have a list of explicit observations, derived from a peer's messages.
   284	
   285	    From there, deductive observations can be made by establishing logical relationships between explicit observations.
   286	
   287	    In the future, we can add more levels of reasoning on top of these.
   288	
   289	    All of a peer's observations are stored as documents in a collection. These documents can be queried in various ways
   290	    to produce this Representation object.
   291	
   292	    Additionally, a "working representation" is a version of this data structure representing the most recent observations
   293	    within a single session.
   294	
   295	    A representation can have a maximum number of observations, which is applied individually to each level of reasoning.
   296	    If a maximum is set, observations are added and removed in FIFO order.
   297	    """
   298	
   299	    explicit: list[ExplicitObservation] = Field(
   300	        description="Facts LITERALLY stated by the user - direct quotes or clear paraphrases only, no interpretation or inference. Example: ['The user is 25 years old', 'The user has a dog']",
   301	        default_factory=list,
   302	    )
   303	    deductive: list[DeductiveObservation] = Field(
   304	        description="Conclusions that MUST be true given explicit facts and premises - strict logical necessities. Each deduction should have premises and a single conclusion.",
   305	        default_factory=list,
   306	    )
   307	    inductive: list[InductiveObservation] = Field(
   308	        description="Patterns, generalizations, and personality insights inferred from multiple observations. Higher-level reasoning created by the Dreamer agent.",
   309	        default_factory=list,
   310	    )
   311	    contradiction: list[ContradictionObservation] = Field(
   312	        description="Conflicting statements made by the user that need clarification. The dialectic agent should surface these when relevant.",
   313	        default_factory=list,
   314	    )
   315	
   316	    def is_empty(self) -> bool:
   317	        """
   318	        Check if the representation is empty.
   319	        """
   320	        return (
   321	            len(self.explicit) == 0
   322	            and len(self.deductive) == 0
   323	            and len(self.inductive) == 0
   324	            and len(self.contradiction) == 0
   325	        )
   326	
   327	    def len(self) -> int:
   328	        """
   329	        Return the total number of observations in the representation.
   330	        """
   331	        return (
   332	            len(self.explicit)
   333	            + len(self.deductive)
   334	            + len(self.inductive)
   335	            + len(self.contradiction)
   336	        )
   337	
   338	    def diff_representation(self, other: "Representation") -> "Representation":
   339	        """
   340	        Given this and another representation, return a new representation with only observations that are unique to the other.
   341	        Note that this only removes literal duplicates, not semantically equivalent ones.
   342	        """
   343	        diff = Representation()
   344	        diff.explicit = [o for o in other.explicit if o not in self.explicit]
   345	        diff.deductive = [o for o in other.deductive if o not in self.deductive]
   346	        diff.inductive = [o for o in other.inductive if o not in self.inductive]
   347	        diff.contradiction = [
   348	            o for o in other.contradiction if o not in self.contradiction
   349	        ]
   350	        return diff
   351	
   352	    def merge_representation(
   353	        self, other: "Representation", max_observations: int | None = None
   354	    ):
   355	        """
   356	        Merge another representation object into this one.
   357	        This will automatically deduplicate explicit, deductive, inductive, and contradiction observations.
   358	        This *preserves order* of observations so that they retain FIFO order.
   359	
   360	        NOTE: observations with the *same* timestamp will not have order preserved.

>nl -ba src/models.py | sed -n '423,560p'

Chunk ID: 031fe9
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1531
Output:
   423	    __table_args__ = (
   424	        CheckConstraint("length(id) = 21", name="id_length"),
   425	        CheckConstraint("length(content) <= 65535", name="content_length"),
   426	        CheckConstraint("id ~ '^[A-Za-z0-9_-]+$'", name="id_format"),
   427	        # Composite foreign key constraint for collections
   428	        ForeignKeyConstraint(
   429	            ["observer", "observed", "workspace_name"],
   430	            [
   431	                "collections.observer",
   432	                "collections.observed",
   433	                "collections.workspace_name",
   434	            ],
   435	        ),
   436	        # Composite foreign key constraint for observer peer
   437	        ForeignKeyConstraint(
   438	            ["observer", "workspace_name"],
   439	            ["peers.name", "peers.workspace_name"],
   440	        ),
   441	        # Composite foreign key constraint for observed peer
   442	        ForeignKeyConstraint(
   443	            ["observed", "workspace_name"],
   444	            ["peers.name", "peers.workspace_name"],
   445	        ),
   446	        # Composite foreign key constraint for sessions
   447	        ForeignKeyConstraint(
   448	            ["session_name", "workspace_name"],
   449	            ["sessions.name", "sessions.workspace_name"],
   450	        ),
   451	        # HNSW index on embedding column
   452	        Index(
   453	            "ix_documents_embedding_hnsw",
   454	            "embedding",
   455	            postgresql_using="hnsw",  # HNSW index type
   456	            postgresql_with={"m": 16, "ef_construction": 64},  # HNSW parameters
   457	            postgresql_ops={
   458	                "embedding": "vector_cosine_ops"
   459	            },  # Cosine distance operator
   460	        ),
   461	        # GIN index for efficient tree traversal (finding children by source IDs)
   462	        Index(
   463	            "ix_documents_source_ids_gin",
   464	            "source_ids",
   465	            postgresql_using="gin",
   466	        ),
   467	        # Composite index for efficient reconciliation queries
   468	        Index(
   469	            "ix_documents_sync_state_last_sync_at",
   470	            "sync_state",
   471	            "last_sync_at",
   472	        ),
   473	    )
   474	
   475	
   476	@final
   477	class QueueItem(Base):
   478	    __tablename__: str = "queue"
   479	    id: Mapped[int] = mapped_column(
   480	        BigInteger, Identity(), primary_key=[REDACTED] autoincrement=True
   481	    )
   482	    session_id: Mapped[str | None] = mapped_column(
   483	        ForeignKey("sessions.id"), nullable=True, index=True
   484	    )
   485	    work_unit_key: Mapped[str] = mapped_column(TEXT, nullable=False)
   486	
   487	    task_type: Mapped[TaskType] = mapped_column(TEXT, nullable=False)
   488	    payload: Mapped[dict[str, Any]] = mapped_column(JSONB, nullable=False)
   489	    processed: Mapped[bool] = mapped_column(
   490	        Boolean, default=False, server_default=text("false"), index=True
   491	    )
   492	    error: Mapped[str | None] = mapped_column(TEXT, nullable=True)
   493	    created_at: Mapped[datetime.datetime] = mapped_column(
   494	        DateTime(timezone=True), server_default=func.now(), index=True
   495	    )
   496	    workspace_name: Mapped[str | None] = mapped_column(
   497	        ForeignKey("workspaces.name"), nullable=True, index=True
   498	    )
   499	    message_id: Mapped[int | None] = mapped_column(
   500	        BigInteger, ForeignKey("messages.id"), nullable=True
   501	    )
   502	
   503	    __table_args__ = (
   504	        Index(
   505	            "ix_queue_message_id_not_null",
   506	            "message_id",
   507	            postgresql_where=text("message_id IS NOT NULL"),
   508	        ),
   509	        Index(
   510	            "ix_queue_work_unit_key_processed_id",
   511	            "work_unit_key",
   512	            "processed",
   513	            "id",
   514	        ),
   515	        # Partial unique index for reconciler task deduplication
   516	        Index(
   517	            "uq_queue_reconciler_pending_work_unit_key",
   518	            "work_unit_key",
   519	            unique=True,
   520	            postgresql_where=text("task_type = 'reconciler' AND processed = false"),
   521	        ),
   522	        # Partial unique index for dream task deduplication
   523	        Index(
   524	            "uq_queue_dream_pending_work_unit_key",
   525	            "work_unit_key",
   526	            unique=True,
   527	            postgresql_where=text("task_type = 'dream' AND processed = false"),
   528	        ),
   529	    )
   530	
   531	    def __repr__(self) -> str:
   532	        return f"QueueItem(id={self.id}, session_id={self.session_id}, work_unit_key=[REDACTED] task_type={self.task_type}, payload={self.payload}, processed={self.processed}, workspace_name={self.workspace_name}, message_id={self.message_id})"
   533	
   534	
   535	@final
   536	class ActiveQueueSession(Base):
   537	    __tablename__: str = "active_queue_sessions"
   538	
   539	    id: Mapped[str] = mapped_column(TEXT, default=generate_nanoid, primary_key=[REDACTED]
   540	
   541	    work_unit_key: Mapped[str] = mapped_column(TEXT, unique=True)
   542	
   543	    last_updated: Mapped[datetime.datetime] = mapped_column(
   544	        DateTime(timezone=True), server_default=func.now(), onupdate=func.now()
   545	    )
   546	
   547	
   548	@final
   549	class WebhookEndpoint(Base):
   550	    __tablename__: str = "webhook_endpoints"
   551	    id: Mapped[str] = mapped_column(TEXT, default=generate_nanoid, primary_key=[REDACTED]
   552	    workspace_name: Mapped[str] = mapped_column(
   553	        ForeignKey("workspaces.name"), nullable=False, index=True
   554	    )
   555	    url: Mapped[str] = mapped_column(TEXT, nullable=False)
   556	    created_at: Mapped[datetime.datetime] = mapped_column(
   557	        DateTime(timezone=True), server_default=func.now()
   558	    )
   559	
   560	    workspace = relationship("Workspace", back_populates="webhook_endpoints")

>nl -ba src/crud/representation.py | sed -n '1,260p'

Chunk ID: 435b07
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2864
Output:
     1	from __future__ import annotations
     2	
     3	import datetime
     4	import logging
     5	import time
     6	from contextlib import suppress
     7	from typing import Any
     8	
     9	from sqlalchemy import select
    10	from sqlalchemy.ext.asyncio import AsyncSession
    11	
    12	from src import crud, exceptions, models, schemas
    13	from src.config import settings
    14	from src.dependencies import tracked_db
    15	from src.dreamer.dream_scheduler import check_and_schedule_dream
    16	from src.embedding_client import embedding_client
    17	from src.schemas import ResolvedConfiguration
    18	from src.telemetry.logging import accumulate_metric
    19	from src.utils.formatting import format_datetime_utc
    20	from src.utils.representation import (
    21	    DeductiveObservation,
    22	    ExplicitObservation,
    23	    Representation,
    24	)
    25	
    26	logger = logging.getLogger(__name__)
    27	
    28	
    29	def _observation_text(obs: ExplicitObservation | DeductiveObservation) -> str:
    30	    """Return the canonical text payload for an explicit or deductive observation."""
    31	    return obs.conclusion if isinstance(obs, DeductiveObservation) else obs.content
    32	
    33	
    34	def _normalized_observation(
    35	    obs: ExplicitObservation | DeductiveObservation,
    36	) -> ExplicitObservation | DeductiveObservation:
    37	    """Return an observation with its persisted/embed text normalized."""
    38	    text = _observation_text(obs).strip()
    39	    if isinstance(obs, DeductiveObservation):
    40	        return obs.model_copy(update={"conclusion": text})
    41	    return obs.model_copy(update={"content": text})
    42	
    43	
    44	class RepresentationManager:
    45	    """Unified manager for representation and document queries."""
    46	
    47	    def __init__(
    48	        self,
    49	        workspace_name: str,
    50	        *,
    51	        observer: str,
    52	        observed: str,
    53	    ) -> None:
    54	        self.workspace_name: str = workspace_name
    55	        self.observer: str = observer
    56	        self.observed: str = observed
    57	
    58	    async def save_representation(
    59	        self,
    60	        representation: Representation,
    61	        message_ids: list[int],
    62	        session_name: str,
    63	        message_created_at: datetime.datetime,
    64	        message_level_configuration: ResolvedConfiguration,
    65	    ) -> int:
    66	        """
    67	        Save Representation objects to the collection as a set of documents.
    68	
    69	        Args:
    70	            representation: Representation object
    71	            message_ids: Message ID range to link with observations
    72	            session_name: Session name to link with existing summary context
    73	            message_created_at: Timestamp when the message was created
    74	
    75	        Returns:
    76	            The number of *new documents saved*
    77	        """
    78	
    79	        new_documents = 0
    80	
    81	        if not representation.deductive and not representation.explicit:
    82	            logger.debug("No observations to save")
    83	            return new_documents
    84	
    85	        all_observations = [
    86	            _normalized_observation(obs)
    87	            for obs in representation.deductive + representation.explicit
    88	            if _observation_text(obs).strip()
    89	        ]
    90	        if not all_observations:
    91	            logger.debug("No non-empty observations to save")
    92	            return new_documents
    93	
    94	        # Batch embed all observations
    95	        batch_embed_start = time.perf_counter()
    96	
    97	        observation_texts = [_observation_text(obs) for obs in all_observations]
    98	        try:
    99	            embeddings = await embedding_client.simple_batch_embed(observation_texts)
   100	        except ValueError as e:
   101	            raise exceptions.ValidationException(
   102	                "Observation content exceeds maximum token limit of "
   103	                + f"{settings.EMBEDDING.MAX_INPUT_TOKENS}."
   104	            ) from e
   105	
   106	        batch_embed_duration = (time.perf_counter() - batch_embed_start) * 1000
   107	        accumulate_metric(
   108	            f"deriver_{message_ids[-1]}_{self.observer}",
   109	            "embed_new_observations",
   110	            batch_embed_duration,
   111	            "ms",
   112	        )
   113	
   114	        # Batch create document objects
   115	        create_document_start = time.perf_counter()
   116	        async with tracked_db("representation_manager.save_representation") as db:
   117	            new_documents = await self._save_representation_internal(
   118	                db,
   119	                all_observations,
   120	                embeddings,
   121	                message_ids,
   122	                session_name,
   123	                message_created_at,
   124	                message_level_configuration,
   125	            )
   126	
   127	        create_document_duration = (time.perf_counter() - create_document_start) * 1000
   128	        accumulate_metric(
   129	            f"deriver_{message_ids[-1]}_{self.observer}",
   130	            "save_new_observations",
   131	            create_document_duration,
   132	            "ms",
   133	        )
   134	
   135	        return new_documents
   136	
   137	    async def _save_representation_internal(
   138	        self,
   139	        db: AsyncSession,
   140	        all_observations: list[ExplicitObservation | DeductiveObservation],
   141	        embeddings: list[list[float]],
   142	        message_ids: list[int],
   143	        session_name: str,
   144	        message_created_at: datetime.datetime,
   145	        message_level_configuration: ResolvedConfiguration,
   146	    ) -> int:
   147	        # get_or_create_collection already handles IntegrityError with rollback and a retry
   148	        collection = await crud.get_or_create_collection(
   149	            db,
   150	            self.workspace_name,
   151	            observer=self.observer,
   152	            observed=self.observed,
   153	        )
   154	
   155	        # Prepare all documents for bulk creation
   156	        documents_to_create: list[schemas.DocumentCreate] = []
   157	        for obs, embedding in zip(all_observations, embeddings, strict=True):
   158	            # NOTE: will add additional levels of reasoning in the future
   159	            if isinstance(obs, DeductiveObservation):
   160	                obs_level = "deductive"
   161	                obs_content = obs.conclusion
   162	                obs_premises = obs.premises
   163	            else:
   164	                obs_level = "explicit"
   165	                obs_content = obs.content
   166	                obs_premises = None
   167	
   168	            metadata: schemas.DocumentMetadata = schemas.DocumentMetadata(
   169	                message_ids=message_ids,
   170	                premises=obs_premises,
   171	                message_created_at=format_datetime_utc(message_created_at),
   172	            )
   173	
   174	            documents_to_create.append(
   175	                schemas.DocumentCreate(
   176	                    content=obs_content,
   177	                    session_name=session_name,
   178	                    level=obs_level,
   179	                    metadata=metadata,
   180	                    embedding=embedding,
   181	                )
   182	            )
   183	
   184	        # Use bulk creation with optional duplicate detection
   185	        accepted_documents = await crud.create_documents(
   186	            db,
   187	            documents_to_create,
   188	            self.workspace_name,
   189	            observer=self.observer,
   190	            observed=self.observed,
   191	            deduplicate=settings.DERIVER.DEDUPLICATE,
   192	        )
   193	
   194	        if message_level_configuration.dream.enabled:
   195	            try:
   196	                await check_and_schedule_dream(db, collection)
   197	            except Exception as e:
   198	                logger.warning(f"Failed to check dream scheduling: {e}")
   199	
   200	        return len(accepted_documents)
   201	
   202	    async def get_working_representation(
   203	        self,
   204	        *,
   205	        db: AsyncSession | None = None,
   206	        session_name: str | None = None,
   207	        include_semantic_query: str | None = None,
   208	        embedding: list[float] | None = None,
   209	        semantic_search_top_k: int | None = None,
   210	        semantic_search_max_distance: float | None = None,
   211	        include_most_derived: bool = False,
   212	        max_observations: int = settings.DERIVER.WORKING_REPRESENTATION_MAX_OBSERVATIONS,
   213	    ) -> Representation:
   214	        """
   215	        Get working representation with flexible query options.
   216	
   217	        Args:
   218	            db: Optional database session. If provided, uses it directly;
   219	                otherwise creates a new session via tracked_db.
   220	            session_name: Optional session to filter by
   221	            include_semantic_query: Query for semantic search
   222	            embedding: Pre-computed embedding for the semantic query.
   223	            semantic_search_top_k: Number of semantic results
   224	            semantic_search_max_distance: Maximum distance for semantic search
   225	            include_most_derived: Include most derived observations
   226	            max_observations: Maximum total observations to return
   227	
   228	        Returns:
   229	            Representation combining various query strategies
   230	        """
   231	        if include_semantic_query and embedding is None:
   232	            with suppress(Exception):
   233	                # Best-effort precompute
   234	                embedding = await embedding_client.embed(include_semantic_query)
   235	
   236	        if db is not None:
   237	            return await self._get_working_representation_internal(
   238	                db,
   239	                session_name=session_name,
   240	                include_semantic_query=include_semantic_query,
   241	                embedding=embedding,
   242	                semantic_search_top_k=semantic_search_top_k,
   243	                semantic_search_max_distance=semantic_search_max_distance,
   244	                include_most_derived=include_most_derived,
   245	                max_observations=max_observations,
   246	            )
   247	
   248	        async with tracked_db(
   249	            "representation_manager.get_working_representation"
   250	        ) as new_db:
   251	            return await self._get_working_representation_internal(
   252	                new_db,
   253	                session_name=session_name,
   254	                include_semantic_query=include_semantic_query,
   255	                embedding=embedding,
   256	                semantic_search_top_k=semantic_search_top_k,
   257	                semantic_search_max_distance=semantic_search_max_distance,
   258	                include_most_derived=include_most_derived,
   259	                max_observations=max_observations,
   260	            )

>nl -ba src/utils/summarizer.py | sed -n '90,240p'

Chunk ID: c8972e
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1680
Output:
    90	
    91	
    92	# The types of summary to store in the session metadata
    93	class SummaryType(Enum):
    94	    SHORT = "honcho_chat_summary_short"
    95	    LONG = "honcho_chat_summary_long"
    96	
    97	
    98	def short_summary_prompt(
    99	    formatted_messages: str,
   100	    output_words: int,
   101	    previous_summary_text: str,
   102	) -> str:
   103	    """Generate the short summary prompt."""
   104	    return c(f"""
   105	You are a system that summarizes parts of a conversation to create a concise and accurate summary. Focus on capturing:
   106	
   107	1. Key facts and information shared (**Capture as many explicit facts as possible**)
   108	2. User preferences, opinions, and questions
   109	3. Important context and requests
   110	4. Core topics discussed
   111	
   112	If there is a previous summary, ALWAYS make your new summary inclusive of both it and the new messages, therefore capturing the ENTIRE conversation. Prioritize key facts across the entire conversation.
   113	
   114	Provide a concise, factual summary that captures the essence of the conversation. Your summary should be detailed enough to serve as context for future messages, but brief enough to be helpful. Prefer a thorough chronological narrative over a list of bullet points.
   115	
   116	Return only the summary without any explanation or meta-commentary.
   117	
   118	<previous_summary>
   119	{previous_summary_text}
   120	</previous_summary>
   121	
   122	<conversation>
   123	{formatted_messages}
   124	</conversation>
   125	
   126	Hard limit: {output_words} words maximum. If needed, drop lower-priority detail to stay within the limit.
   127	""")
   128	
   129	
   130	def long_summary_prompt(
   131	    formatted_messages: str,
   132	    output_words: int,
   133	    previous_summary_text: str,
   134	) -> str:
   135	    """Generate the long summary prompt."""
   136	    return c(f"""
   137	You are a system that creates thorough, comprehensive summaries of conversations. Focus on capturing:
   138	
   139	1. Key facts and information shared (**Capture as many explicit facts as possible**)
   140	2. User preferences, opinions, and questions
   141	3. Important context and requests
   142	4. Core topics discussed in detail
   143	5. User's apparent emotional state and personality traits
   144	6. Important themes and patterns across the conversation
   145	
   146	If there is a previous summary, ALWAYS make your new summary inclusive of both it and the new messages, therefore capturing the ENTIRE conversation. Prioritize key facts across the entire conversation.
   147	
   148	Provide a thorough and detailed summary that captures the essence of the conversation. Your summary should serve as a comprehensive record of the important information in this conversation. Prefer an exhaustive chronological narrative over a list of bullet points.
   149	
   150	Return only the summary without any explanation or meta-commentary.
   151	
   152	<previous_summary>
   153	{previous_summary_text}
   154	</previous_summary>
   155	
   156	<conversation>
   157	{formatted_messages}
   158	</conversation>
   159	
   160	Hard limit: {output_words} words maximum. If needed, drop lower-priority detail to stay within the limit.
   161	""")
   162	
   163	
   164	@cache
   165	def estimate_short_summary_prompt_tokens() -> int:
   166	    """Estimate tokens for the short summary prompt (without messages/previous_summary)."""
   167	    try:
   168	        return estimate_tokens(
   169	            short_summary_prompt(
   170	                formatted_messages="",
   171	                output_words=0,
   172	                previous_summary_text="",
   173	            )
   174	        )
   175	    except Exception:
   176	        # Return a rough estimate if estimation fails
   177	        return 200
   178	
   179	
   180	@cache
   181	def estimate_long_summary_prompt_tokens() -> int:
   182	    """Estimate tokens for the long summary prompt (without messages/previous_summary)."""
   183	    try:
   184	        return estimate_tokens(
   185	            long_summary_prompt(
   186	                formatted_messages="",
   187	                output_words=0,
   188	                previous_summary_text="",
   189	            )
   190	        )
   191	    except Exception:
   192	        # Return a rough estimate if estimation fails
   193	        return 200
   194	
   195	
   196	@conditional_observe(name="Create Short Summary")
   197	async def create_short_summary(
   198	    formatted_messages: str,
   199	    input_tokens: int,
   200	    previous_summary: str | None = None,
   201	) -> HonchoLLMCallResponse[str]:
   202	    # input_tokens indicates how many tokens the message list + previous summary take up
   203	    # we want to optimize short summaries to be smaller than the actual content being summarized
   204	    # so we ask the agent to produce a word count roughly equal to either the input, or the max
   205	    # size if the input is larger. the word/token ratio is roughly 4:3 so we multiply by 0.75.
   206	    # LLMs *seem* to respond better to getting asked for a word count but should workshop this.
   207	    output_words = int(min(input_tokens, settings.SUMMARY.MAX_TOKENS_SHORT) * 0.75)
   208	
   209	    if previous_summary:
   210	        previous_summary_text = previous_summary
   211	    else:
   212	        previous_summary_text = "There is no previous summary -- the messages are the beginning of the conversation."
   213	
   214	    prompt = short_summary_prompt(
   215	        formatted_messages, output_words, previous_summary_text
   216	    )
   217	
   218	    return await honcho_llm_call(
   219	        model_config=_get_summary_model_config(),
   220	        prompt=prompt,
   221	        max_tokens=[REDACTED]
   222	    )
   223	
   224	
   225	@conditional_observe(name="Create Long Summary")
   226	async def create_long_summary(
   227	    formatted_messages: str,
   228	    previous_summary: str | None = None,
   229	) -> HonchoLLMCallResponse[str]:
   230	    # the word/token ratio is roughly 4:3 so we multiply by 0.75.
   231	    # LLMs *seem* to respond better to getting asked for a word count but should workshop this.
   232	    output_words = int(settings.SUMMARY.MAX_TOKENS_LONG * 0.75)
   233	
   234	    if previous_summary:
   235	        previous_summary_text = previous_summary
   236	    else:
   237	        previous_summary_text = "There is no previous summary -- the messages are the beginning of the conversation."
   238	
   239	    prompt = long_summary_prompt(
   240	        formatted_messages, output_words, previous_summary_text

>nl -ba src/dialectic/core.py | sed -n '70,240p'

Chunk ID: 789bf3
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2016
Output:
    70	        Initialize the dialectic agent.
    71	
    72	        Args:
    73	            workspace_name: Workspace identifier
    74	            session_name: Session identifier (may be None for global queries)
    75	            observer: The peer making the query
    76	            observed: The peer being queried about
    77	            observer_peer_card: Biographical information about the observer
    78	            observed_peer_card: Biographical information about the observed peer
    79	            metric_key: Optional key for logging metrics (if provided, agent won't log separately)
    80	            reasoning_level: Level of reasoning to apply
    81	        """
    82	        self.workspace_name: str = workspace_name
    83	        self.session_name: str | None = session_name
    84	        self.observer: str = observer
    85	        self.observed: str = observed
    86	        self.observer_peer_card: list[str] | None = observer_peer_card
    87	        self.observed_peer_card: list[str] | None = observed_peer_card
    88	        self.metric_key: str | None = metric_key
    89	        self.reasoning_level: ReasoningLevel = reasoning_level
    90	
    91	        # Initialize conversation history with system prompt
    92	        self.messages: list[dict[str, str]] = [
    93	            {
    94	                "role": "system",
    95	                "content": prompts.agent_system_prompt(
    96	                    observer, observed, observer_peer_card, observed_peer_card
    97	                ),
    98	            }
    99	        ]
   100	        self._session_history_initialized: bool = False
   101	        self._prefetched_conclusion_count: int = 0
   102	        self._run_id: str = str(uuid.uuid4())[
   103	            :8
   104	        ]  # Always generate for event correlation
   105	
   106	    async def _initialize_session_history(self) -> None:
   107	        """Fetch and inject session history into the system prompt if configured."""
   108	        if self._session_history_initialized:
   109	            return
   110	        self._session_history_initialized = True
   111	
   112	        max_tokens = settings.DIALECTIC.SESSION_HISTORY_MAX_TOKENS
   113	        if max_tokens == 0 or not self.session_name:
   114	            return
   115	
   116	        # Fetch recent messages up to the token limit
   117	        stmt = await crud.get_messages(
   118	            workspace_name=self.workspace_name,
   119	            session_name=self.session_name,
   120	            token_limit=[REDACTED]
   121	            reverse=False,  # chronological order
   122	        )
   123	        async with tracked_db("dialectic.session_history") as db:
   124	            result = await db.execute(stmt)
   125	            messages = result.scalars().all()
   126	
   127	            if not messages:
   128	                return
   129	
   130	            # Format messages for injection (must access ORM attrs before session closes)
   131	            formatted_messages: list[str] = []
   132	            for msg in messages:
   133	                formatted = format_new_turn_with_timestamp(
   134	                    msg.content, msg.created_at, msg.peer_name
   135	                )
   136	                formatted_messages.append(formatted)
   137	
   138	        session_history_section = (
   139	            "\n\n## SESSION HISTORY\n\n"
   140	            "The following is the recent conversation history from this session. "
   141	            "Use this as immediate context when answering the query.\n\n"
   142	            "<session_history>\n"
   143	            f"{chr(10).join(formatted_messages)}\n"
   144	            "</session_history>"
   145	        )
   146	
   147	        # Append session history to the system prompt
   148	        self.messages[0]["content"] += session_history_section
   149	
   150	    async def _prefetch_relevant_observations(self, query: str) -> str | None:
   151	        """
   152	        Prefetch semantically relevant observations for the query.
   153	
   154	        This provides immediate context to the agent without requiring
   155	        tool calls, improving response quality and speed.
   156	
   157	        Performs two separate searches to prevent retrieval dilution:
   158	        - Explicit observations (produced by deriver)
   159	        - Higher-level observations (produced in dreaming/background/chat)
   160	
   161	        The number of observations fetched depends on reasoning level:
   162	        - minimal: 10 of each type (reduced context for cost savings)
   163	        - all others: 25 of each type
   164	
   165	        Args:
   166	            query: The user's query
   167	
   168	        Returns:
   169	            Formatted observations string or None if no observations found
   170	        """
   171	        # Use reduced prefetch for minimal reasoning to save tokens
   172	        prefetch_limit = 10 if self.reasoning_level == "minimal" else 25
   173	
   174	        try:
   175	            # Pre-compute embedding once for both searches (no DB needed)
   176	            query_embedding = await embedding_client.embed(query)
   177	
   178	            # search_memory manages its own short-lived DB sessions so no
   179	            # connection is held during external vector-store calls.
   180	            explicit_repr = await search_memory(
   181	                workspace_name=self.workspace_name,
   182	                observer=self.observer,
   183	                observed=self.observed,
   184	                query=query,
   185	                limit=prefetch_limit,
   186	                levels=["explicit"],
   187	                embedding=query_embedding,
   188	            )
   189	
   190	            derived_repr = await search_memory(
   191	                workspace_name=self.workspace_name,
   192	                observer=self.observer,
   193	                observed=self.observed,
   194	                query=query,
   195	                limit=prefetch_limit,
   196	                levels=["deductive", "inductive", "contradiction"],
   197	                embedding=query_embedding,
   198	            )
   199	
   200	            if explicit_repr.is_empty() and derived_repr.is_empty():
   201	                return None
   202	
   203	            # Count prefetched conclusions for telemetry
   204	            explicit_count = len(explicit_repr.explicit) + len(explicit_repr.deductive)
   205	            derived_count = len(derived_repr.explicit) + len(derived_repr.deductive)
   206	            self._prefetched_conclusion_count = explicit_count + derived_count
   207	
   208	            # Format as two separate sections
   209	            parts: list[str] = []
   210	
   211	            if not explicit_repr.is_empty():
   212	                parts.append(explicit_repr.format_as_markdown(include_ids=False))
   213	
   214	            if not derived_repr.is_empty():
   215	                # Include IDs for derived so agent can use get_reasoning_chain
   216	                parts.append(derived_repr.format_as_markdown(include_ids=True))
   217	
   218	            return "\n".join(parts)
   219	
   220	        except Exception as e:
   221	            logger.warning(f"Failed to prefetch observations: {e}")
   222	            return None
   223	
   224	    async def _prepare_query(
   225	        self, query: str
   226	    ) -> tuple[Callable[[str, dict[str, Any]], Any], str, str | None, float]:
   227	        """
   228	        Prepare common state for answering a query.
   229	
   230	        Handles session history initialization, metrics setup, observation prefetching,
   231	        user message construction, and tool executor creation.
   232	
   233	        Args:
   234	            query: The question to answer about the peer
   235	
   236	        Returns:
   237	            A tuple of (tool_executor, task_name, run_id, start_time)
   238	        """
   239	        await self._initialize_session_history()
   240

>nl -ba src/dialectic/core.py | sed -n '240,430p'

Chunk ID: 1b3b1e
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2148
Output:
   240	
   241	        run_id: str | None = None
   242	        if self.metric_key:
   243	            task_name = self.metric_key
   244	        else:
   245	            run_id = str(uuid.uuid4())[:8]
   246	            task_name = f"dialectic_chat_{run_id}"
   247	        start_time = time.perf_counter()
   248	
   249	        accumulate_metric(
   250	            task_name,
   251	            "context",
   252	            (
   253	                f"workspace: {self.workspace_name}\n"
   254	                f"session: {self.session_name or '(global)'}\n"
   255	                f"observer: {self.observer}\n"
   256	                f"observed: {self.observed}\n"
   257	                f"reasoning_level: {self.reasoning_level}"
   258	            ),
   259	            "blob",
   260	        )
   261	        accumulate_metric(task_name, "query", query, "blob")
   262	
   263	        prefetched_observations = await self._prefetch_relevant_observations(query)
   264	
   265	        if prefetched_observations:
   266	            user_content = (
   267	                f"Query: {query}\n\n"
   268	                f"## Relevant Observations (prefetched)\n"
   269	                f"The following observations were found to be semantically relevant to your query. "
   270	                f"Use these as primary context. You may still use tools to find additional information if needed.\n\n"
   271	                f"{prefetched_observations}"
   272	            )
   273	            accumulate_metric(
   274	                task_name, "prefetched_observations", prefetched_observations, "blob"
   275	            )
   276	        else:
   277	            user_content = f"Query: {query}"
   278	
   279	        self.messages.append({"role": "user", "content": user_content})
   280	
   281	        tool_executor: Callable[
   282	            [str, dict[str, Any]], Any
   283	        ] = await create_tool_executor(
   284	            workspace_name=self.workspace_name,
   285	            session_name=self.session_name,
   286	            observer=self.observer,
   287	            observed=self.observed,
   288	            history_token_limit=[REDACTED]
   289	            run_id=self._run_id,
   290	            agent_type="dialectic",
   291	            parent_category="dialectic",
   292	        )
   293	
   294	        return tool_executor, task_name, run_id, start_time
   295	
   296	    def _log_response_metrics(
   297	        self,
   298	        task_name: str,
   299	        run_id: str | None,
   300	        start_time: float,
   301	        response_content: str,
   302	        input_tokens: int,
   303	        output_tokens: int,
   304	        cache_read_input_tokens: int | None,
   305	        cache_creation_input_tokens: int | None,
   306	        tool_calls_count: int,
   307	        thinking_content: str | None,
   308	        iterations: int,
   309	    ) -> None:
   310	        """
   311	        Log metrics common to both streaming and non-streaming responses.
   312	
   313	        Args:
   314	            task_name: Metrics task identifier
   315	            run_id: Run identifier (None if using caller-provided metric_key)
   316	            start_time: Start time from time.perf_counter()
   317	            response_content: The full response text
   318	            input_tokens: Input token count (actual from API)
   319	            output_tokens: Output token count (actual from API)
   320	            cache_read_input_tokens: Cache read tokens (if any)
   321	            cache_creation_input_tokens: Cache creation tokens (if any)
   322	            tool_calls_count: Number of tool calls made
   323	            thinking_content: Thinking trace content (if any)
   324	            iterations: Number of iterations in the tool execution loop
   325	        """
   326	        accumulate_metric(task_name, "tool_calls", tool_calls_count, "count")
   327	
   328	        if thinking_content:
   329	            accumulate_metric(task_name, "thinking", thinking_content, "blob")
   330	
   331	        log_token_usage_metrics(
   332	            task_name,
   333	            input_tokens,
   334	            output_tokens,
   335	            cache_read_input_tokens or 0,
   336	            cache_creation_input_tokens or 0,
   337	        )
   338	        accumulate_metric(task_name, "response", response_content, "blob")
   339	
   340	        elapsed_ms = (time.perf_counter() - start_time) * 1000
   341	        accumulate_metric(task_name, "total_duration", elapsed_ms, "ms")
   342	
   343	        if not self.metric_key and run_id is not None:
   344	            log_performance_metrics("dialectic_chat", run_id)
   345	
   346	        # Prometheus metrics
   347	        if settings.METRICS.ENABLED:
   348	            prometheus_metrics.record_dialectic_tokens(
   349	                count=input_tokens,
   350	                token_type=[REDACTED]
   351	                component=DialecticComponents.TOTAL.value,
   352	                reasoning_level=self.reasoning_level,
   353	            )
   354	            prometheus_metrics.record_dialectic_tokens(
   355	                count=output_tokens,
   356	                token_type=[REDACTED]
   357	                component=DialecticComponents.TOTAL.value,
   358	                reasoning_level=self.reasoning_level,
   359	            )
   360	
   361	        # Emit telemetry event
   362	        emit(
   363	            DialecticCompletedEvent(
   364	                run_id=self._run_id,
   365	                workspace_name=self.workspace_name,
   366	                peer_name=self.observed,
   367	                session_name=self.session_name,
   368	                reasoning_level=self.reasoning_level,
   369	                total_iterations=iterations,
   370	                prefetched_conclusion_count=self._prefetched_conclusion_count,
   371	                tool_calls_count=tool_calls_count,
   372	                total_duration_ms=elapsed_ms,
   373	                input_tokens=[REDACTED]
   374	                output_tokens=[REDACTED]
   375	                cache_read_tokens=[REDACTED] or 0,
   376	                cache_creation_tokens=[REDACTED] or 0,
   377	            )
   378	        )
   379	
   380	    async def answer(self, query: str) -> str:
   381	        """
   382	        Answer a query about the peer using agentic tool calling.
   383	
   384	        The agent will:
   385	        1. Receive the query
   386	        2. Use tools to gather relevant context
   387	        3. Synthesize a response grounded in the gathered context
   388	
   389	        Args:
   390	            query: The question to answer about the peer
   391	
   392	        Returns:
   393	            The synthesized answer string
   394	        """
   395	        tool_executor, task_name, run_id, start_time = await self._prepare_query(query)
   396	
   397	        # Get level-specific settings
   398	        level_settings = settings.DIALECTIC.LEVELS[self.reasoning_level]
   399	
   400	        # Use minimal tools for minimal reasoning to reduce cost
   401	        tools = (
   402	            DIALECTIC_TOOLS_MINIMAL
   403	            if self.reasoning_level == "minimal"
   404	            else DIALECTIC_TOOLS
   405	        )
   406	        # Use level-specific max_output_tokens if set, otherwise global default
   407	        max_tokens = (
   408	            level_settings.MAX_OUTPUT_TOKENS
   409	            if level_settings.MAX_OUTPUT_TOKENS is not None
   410	            else settings.DIALECTIC.MAX_OUTPUT_TOKENS
   411	        )
   412	
   413	        response: HonchoLLMCallResponse[str] = await honcho_llm_call(
   414	            model_config=_get_dialectic_level_model_config(self.reasoning_level),
   415	            prompt="",  # Ignored since we pass messages
   416	            max_tokens=[REDACTED]
   417	            tools=tools,
   418	            tool_choice=level_settings.TOOL_CHOICE,
   419	            tool_executor=tool_executor,
   420	            max_tool_iterations=level_settings.MAX_TOOL_ITERATIONS,
   421	            messages=self.messages,
   422	            track_name="Dialectic Agent",
   423	            max_input_tokens=[REDACTED]
   424	            trace_name="dialectic_chat",
   425	        )
   426	
   427	        self._log_response_metrics(
   428	            task_name=task_name,
   429	            run_id=run_id,
   430	            start_time=start_time,

>nl -ba src/dreamer/orchestrator.py | sed -n '1,210p'

Chunk ID: c4d050
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2289
Output:
     1	"""
     2	Dream orchestrator for the specialist-based architecture.
     3	
     4	This module coordinates the full dream cycle:
     5	0. [Optional] Surprisal sampling: Pre-filter observations by geometric surprisal
     6	1. Run deduction specialist (self-directed exploration, creates deductive observations)
     7	2. Run induction specialist (self-directed exploration, creates inductive observations)
     8	
     9	Specialists are self-directed agents that explore the observation space and create
    10	higher-level observations. When surprisal sampling finds interesting observations,
    11	they're passed as hints, but specialists are free to follow the evidence wherever it leads.
    12	"""
    13	
    14	from __future__ import annotations
    15	
    16	import logging
    17	import time
    18	import uuid
    19	from dataclasses import dataclass
    20	from datetime import datetime, timezone
    21	from typing import Any
    22	
    23	import sentry_sdk
    24	from sqlalchemy import func, select
    25	
    26	from src import crud, models
    27	from src.config import settings
    28	from src.dependencies import tracked_db
    29	from src.dreamer.specialists import SPECIALISTS, SpecialistResult
    30	from src.dreamer.surprisal import SurprisalScore  # type: ignore
    31	from src.exceptions import SpecialistExecutionError, SurprisalError
    32	from src.schemas import DreamType
    33	from src.telemetry.events import DreamRunEvent, emit
    34	from src.telemetry.logging import (
    35	    accumulate_metric,
    36	    log_performance_metrics,
    37	)
    38	from src.utils.config_helpers import get_configuration
    39	from src.utils.queue_payload import DreamPayload
    40	
    41	logger = logging.getLogger(__name__)
    42	
    43	
    44	@dataclass
    45	class DreamResult:
    46	    """Result of a dream cycle for telemetry reporting."""
    47	
    48	    # Run identification
    49	    run_id: str
    50	    specialists_run: list[str]
    51	
    52	    # Specialist outcomes
    53	    deduction_success: bool
    54	    induction_success: bool
    55	
    56	    # Surprisal sampling
    57	    surprisal_enabled: bool
    58	    surprisal_conclusion_count: int
    59	
    60	    # Aggregate metrics
    61	    total_iterations: int
    62	    total_duration_ms: float
    63	    input_tokens: int
    64	    output_tokens: int
    65	
    66	
    67	async def run_dream(
    68	    workspace_name: str,
    69	    observer: str,
    70	    observed: str,
    71	    session_name: str | None = None,
    72	) -> DreamResult | None:
    73	    """
    74	    Run a full dream cycle with optional surprisal-based sampling.
    75	
    76	    The dream cycle runs specialists sequentially:
    77	    0. [Optional] Surprisal sampling: Pre-filter observations by geometric surprisal
    78	    1. Deduction specialist: Creates deductive observations from explicit facts
    79	    2. Induction specialist: Creates inductive observations from patterns
    80	
    81	    Uses short-lived DB sessions to avoid holding connections during LLM calls.
    82	
    83	    Args:
    84	        workspace_name: Workspace identifier
    85	        observer: Observer peer name
    86	        observed: Observed peer name
    87	        session_name: Session identifier if specified
    88	    """
    89	    if not settings.DREAM.ENABLED:
    90	        return None
    91	
    92	    run_id = str(uuid.uuid4())[:8]
    93	    task_name = f"dream_orchestrator_{run_id}"
    94	    start_time = time.perf_counter()
    95	
    96	    logger.info(
    97	        f"[{run_id}] Starting dream cycle for {workspace_name}/{observer}/{observed}"
    98	    )
    99	
   100	    # Short-lived DB session for config resolution
   101	    async with tracked_db("dream.config") as db:
   102	        if session_name is not None:
   103	            session = await crud.get_session(
   104	                db, workspace_name=workspace_name, session_name=session_name
   105	            )
   106	        else:
   107	            session = None
   108	
   109	        workspace = await crud.get_workspace(db, workspace_name=workspace_name)
   110	        configuration = get_configuration(None, session, workspace)
   111	    if not configuration.dream.enabled:
   112	        logger.info(
   113	            f"[{run_id}] Dreams disabled for {workspace_name}/{session_name}, skipping dream"
   114	        )
   115	        return None
   116	
   117	    # Track specialist outcomes
   118	    deduction_success = False
   119	    induction_success = False
   120	    surprisal_observation_count = 0
   121	    deduction_result: SpecialistResult | None = None
   122	    induction_result: SpecialistResult | None = None
   123	
   124	    # Phase 0: Surprisal-based sampling (if enabled)
   125	    # Specialists are self-directed by default - hints are optional suggestions
   126	    exploration_hints: list[str] | None = None
   127	
   128	    if settings.DREAM.SURPRISAL.ENABLED:
   129	        logger.info(f"[{run_id}] Phase 0: Computing surprisal scores")
   130	        try:
   131	            from src.dreamer.surprisal import sample_observations_with_surprisal
   132	
   133	            high_surprisal_obs = await sample_observations_with_surprisal(
   134	                workspace_name=workspace_name,
   135	                observer=observer,
   136	                observed=observed,
   137	            )
   138	
   139	            logger.info(
   140	                f"[{run_id}] Surprisal: Found {len(high_surprisal_obs)} high-surprisal observations"
   141	            )
   142	            surprisal_observation_count = len(high_surprisal_obs)
   143	            accumulate_metric(
   144	                task_name, "surprisal_observations", len(high_surprisal_obs), "count"
   145	            )
   146	
   147	            if len(high_surprisal_obs) > 0:
   148	                # Use high-surprisal observations as hints for exploration
   149	                exploration_hints = _create_queries_from_surprisal(high_surprisal_obs)
   150	                logger.info(
   151	                    f"[{run_id}] ✨ SURPRISAL HINTS: Suggesting {len(exploration_hints)} "
   152	                    + "high-surprisal topics for specialists to investigate"
   153	                )
   154	                logger.info(
   155	                    f"[{run_id}] Targeting observations with surprisal range: "
   156	                    + f"{high_surprisal_obs[-1].surprisal:.3f} to {high_surprisal_obs[0].surprisal:.3f}"
   157	                )
   158	            else:
   159	                logger.info(
   160	                    f"[{run_id}] No high-surprisal observations - specialists will explore freely"
   161	                )
   162	
   163	        except SurprisalError as e:
   164	            logger.error(f"[{run_id}] Surprisal sampling failed: {e}", exc_info=True)
   165	            accumulate_metric(task_name, "surprisal_error", str(e), "blob")
   166	            # Specialists will explore freely without hints
   167	
   168	    # Phase 1: Run deduction specialist (manages its own DB sessions)
   169	    logger.info(f"[{run_id}] Phase 1: Running deduction specialist")
   170	    deduction_specialist = SPECIALISTS["deduction"]
   171	    try:
   172	        deduction_result = await deduction_specialist.run(
   173	            workspace_name=workspace_name,
   174	            observer=observer,
   175	            observed=observed,
   176	            session_name=session_name,
   177	            hints=exploration_hints,
   178	            configuration=configuration,
   179	            parent_run_id=run_id,
   180	        )
   181	        logger.info(
   182	            f"[{run_id}] Deduction completed: {deduction_result.content[:200]}..."
   183	        )
   184	        accumulate_metric(
   185	            task_name, "deduction_result", deduction_result.content, "blob"
   186	        )
   187	        deduction_success = deduction_result.success
   188	    except SpecialistExecutionError as e:
   189	        logger.error(f"[{run_id}] Deduction specialist failed: {e}", exc_info=True)
   190	        accumulate_metric(task_name, "deduction_error", str(e), "blob")
   191	
   192	    # Phase 2: Run induction specialist (after deduction so it can see new deductive obs)
   193	    logger.info(f"[{run_id}] Phase 2: Running induction specialist")
   194	    induction_specialist = SPECIALISTS["induction"]
   195	    try:
   196	        induction_result = await induction_specialist.run(
   197	            workspace_name=workspace_name,
   198	            observer=observer,
   199	            observed=observed,
   200	            session_name=session_name,
   201	            hints=exploration_hints,
   202	            configuration=configuration,
   203	            parent_run_id=run_id,
   204	        )
   205	        logger.info(
   206	            f"[{run_id}] Induction completed: {induction_result.content[:200]}..."
   207	        )
   208	        accumulate_metric(
   209	            task_name, "induction_result", induction_result.content, "blob"
   210	        )

>nl -ba src/dreamer/specialists.py | sed -n '300,560p'

Chunk ID: c81285
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2618
Output:
   300	            duration_ms=duration_ms,
   301	            success=True,
   302	            content=response.content,
   303	        )
   304	
   305	
   306	class DeductionSpecialist(BaseSpecialist):
   307	    """
   308	    Creates deductive observations from explicit observations.
   309	
   310	    This specialist:
   311	    1. Explores recent observations and messages to understand what's there
   312	    2. Identifies logical implications, knowledge updates, and contradictions
   313	    3. Creates new deductive observations with premise linkage
   314	    4. Deletes outdated observations
   315	    5. Updates peer card with biographical facts
   316	    """
   317	
   318	    name: str = "deduction"
   319	    peer_card_update_instruction: str = "Update this with `update_peer_card` only for stable biographical/profile facts."
   320	
   321	    def get_tools(self, *, peer_card_enabled: bool = True) -> list[dict[str, Any]]:
   322	        if peer_card_enabled:
   323	            return DEDUCTION_SPECIALIST_TOOLS
   324	        return [
   325	            t
   326	            for t in DEDUCTION_SPECIALIST_TOOLS
   327	            if t["name"] not in PEER_CARD_TOOL_NAMES
   328	        ]
   329	
   330	    def get_model_config(self) -> ConfiguredModelSettings:
   331	        return _require_specialist_model_config(
   332	            settings.DREAM.DEDUCTION_MODEL_CONFIG,
   333	            specialist_name="DREAM DEDUCTION",
   334	        )
   335	
   336	    def get_max_tokens(self) -> int:
   337	        return 8192
   338	
   339	    def get_max_iterations(self) -> int:
   340	        return 12
   341	
   342	    def build_system_prompt(
   343	        self, observed: str, *, peer_card_enabled: bool = True
   344	    ) -> str:
   345	        peer_card_section = ""
   346	        if peer_card_enabled:
   347	            peer_card_section = """
   348	
   349	## PEER CARD (REQUIRED)
   350	
   351	The peer card is a summary of stable biographical facts. You MUST update it when you learn:
   352	- Name, age, location, occupation
   353	- Family members and relationships
   354	- Standing instructions ("call me X", "don't mention Y")
   355	- Core preferences and traits
   356	
   357	Never add temporary event summaries, one-off conclusions, reasoning traces, or contradiction notes.
   358	
   359	Format entries as:
   360	- Plain facts: "Name: Alice", "Works at Google", "Lives in NYC"
   361	- `INSTRUCTION: ...` for standing instructions
   362	- `PREFERENCE: ...` for preferences
   363	- `TRAIT: ...` for personality traits
   364	
   365	Call `update_peer_card` with the complete updated list when you have new biographical info.
   366	Keep it concise (max 40 entries), deduplicated, and current."""
   367	
   368	        return f"""You are a deductive reasoning agent analyzing observations about {observed}.
   369	
   370	## YOUR JOB
   371	
   372	Create deductive observations by finding logical implications in what's already known. Think like a detective connecting evidence.
   373	
   374	## PHASE 1: DISCOVERY
   375	
   376	Explore what's actually in memory. Use these tools freely:
   377	- `get_recent_observations` - See what's been learned recently
   378	- `search_memory` - Search for specific topics
   379	- `search_messages` - See actual conversation content
   380	
   381	Spend a few tool calls understanding the landscape before creating anything.
   382	
   383	## PHASE 2: ACTION
   384	
   385	Once you understand what's there, create observations and clean up:
   386	
   387	### Knowledge Updates (HIGH PRIORITY)
   388	When the same fact has different values at different times:
   389	- "meeting Tuesday" [old] → "meeting moved to Thursday" [new]
   390	- Create a deductive update observation
   391	- DELETE the outdated observation immediately
   392	
   393	### Logical Implications
   394	Extract implicit information:
   395	- "works as SWE at Google" → "has software engineering skills", "employed in tech"
   396	- "has kids ages 5 and 8" → "is a parent", "has school-age children"
   397	
   398	### Contradictions
   399	When statements can't both be true (not just updates), flag them:
   400	- "I love coffee" vs "I hate coffee" → contradiction observation
   401	{peer_card_section}
   402	
   403	## CREATING OBSERVATIONS
   404	
   405	Use `create_observations_deductive`.
   406	
   407	```json
   408	{{
   409	  "observations": [{{
   410	    "content": "The logical conclusion",
   411	    "source_ids": ["id1", "id2"],
   412	    "premises": ["premise 1 text", "premise 2 text"]
   413	  }}]
   414	}}
   415	```
   416	
   417	## RULES
   418	
   419	1. Don't explain your reasoning - just call tools
   420	2. Create observations based on what you ACTUALLY FIND, not what you expect
   421	3. Always include source_ids linking to the observations you're synthesizing
   422	4. Empty or missing source_ids will be rejected
   423	5. Delete outdated observations - don't leave duplicates
   424	6. Quality over quantity - fewer good deductions beat many weak ones"""
   425	
   426	    def build_user_prompt(
   427	        self,
   428	        hints: list[str] | None,
   429	        peer_card: list[str] | None = None,
   430	    ) -> str:
   431	        peer_card_context = self._build_peer_card_context(peer_card)
   432	
   433	        if hints:
   434	            hints_str = "\n".join(f"- {q}" for q in hints[:5])
   435	            return f"""{peer_card_context}Start by exploring recent observations and messages. These topics may be worth investigating:
   436	
   437	{hints_str}
   438	
   439	But follow the evidence - if you find something more interesting, pursue that instead.
   440	
   441	Begin with `get_recent_observations` to see what's there."""
   442	
   443	        return f"""{peer_card_context}Explore the observation space and create deductive observations.
   444	
   445	Start with `get_recent_observations` to see what's been learned recently, then investigate whatever seems most promising.
   446	
   447	Look for:
   448	1. Knowledge updates (same fact, different values over time)
   449	2. Logical implications that haven't been made explicit
   450	3. Contradictions that need flagging
   451	
   452	Go."""
   453	
   454	
   455	class InductionSpecialist(BaseSpecialist):
   456	    """
   457	    Creates inductive observations from explicit and deductive observations.
   458	
   459	    This specialist:
   460	    1. Explores observations to understand what's there
   461	    2. Identifies patterns and generalizations across multiple observations
   462	    3. Creates new inductive observations with source linkage
   463	    4. Updates peer card with high-confidence traits and tendencies
   464	    """
   465	
   466	    name: str = "induction"
   467	    peer_card_update_instruction: str = "Only add highly stable profile traits/preferences; do not copy transient conclusions."
   468	
   469	    def get_tools(self, *, peer_card_enabled: bool = True) -> list[dict[str, Any]]:
   470	        if peer_card_enabled:
   471	            return INDUCTION_SPECIALIST_TOOLS
   472	        return [
   473	            t
   474	            for t in INDUCTION_SPECIALIST_TOOLS
   475	            if t["name"] not in PEER_CARD_TOOL_NAMES
   476	        ]
   477	
   478	    def get_model_config(self) -> ConfiguredModelSettings:
   479	        return _require_specialist_model_config(
   480	            settings.DREAM.INDUCTION_MODEL_CONFIG,
   481	            specialist_name="DREAM INDUCTION",
   482	        )
   483	
   484	    def get_max_tokens(self) -> int:
   485	        return 8192
   486	
   487	    def get_max_iterations(self) -> int:
   488	        return 10
   489	
   490	    def build_system_prompt(
   491	        self, observed: str, *, peer_card_enabled: bool = True
   492	    ) -> str:
   493	        peer_card_section = ""
   494	        if peer_card_enabled:
   495	            peer_card_section = """
   496	
   497	## PEER CARD (REQUIRED)
   498	
   499	After identifying patterns, only update the peer card for durable profile-level traits/preferences:
   500	- `TRAIT: Analytical thinker`
   501	- `TRAIT: Tends to reschedule when stressed`
   502	- `PREFERENCE: Prefers detailed explanations`
   503	
   504	Do NOT add temporary patterns, episode-specific conclusions, or reasoning summaries.
   505	Call `update_peer_card` with the complete deduplicated list only when a durable profile update is warranted.
   506	Keep it concise (max 40 entries)."""
   507	
   508	        return f"""You are an inductive reasoning agent identifying patterns about {observed}.
   509	
   510	## YOUR JOB
   511	
   512	Create inductive observations by finding patterns across multiple observations. Think like a psychologist identifying behavioral tendencies.
   513	
   514	## PHASE 1: DISCOVERY
   515	
   516	Explore broadly to find patterns. Use these tools:
   517	- `get_recent_observations` - Recent learnings
   518	- `search_memory` - Topic-specific search
   519	- `search_messages` - Actual conversation content
   520	
   521	Look at BOTH explicit observations AND deductive ones. Patterns often emerge from synthesizing across both levels.
   522	
   523	## PHASE 2: ACTION
   524	
   525	Create inductive observations when you see patterns:
   526	
   527	### Behavioral Patterns
   528	- "Tends to reschedule meetings when stressed"
   529	- "Makes decisions after consulting with partner"
   530	- "Projects follow: enthusiasm → doubt → completion"
   531	
   532	### Preferences
   533	- "Prefers morning meetings"
   534	- "Likes detailed technical explanations"
   535	
   536	### Personality Traits
   537	- "Generally optimistic about outcomes"
   538	- "Detail-oriented in planning"
   539	
   540	### Temporal Patterns
   541	- "Career goals have remained consistent"
   542	- "Living situation changes frequently"
   543	{peer_card_section}
   544	
   545	## CREATING OBSERVATIONS
   546	
   547	Use `create_observations_inductive`.
   548	
   549	```json
   550	{{
   551	  "observations": [{{
   552	    "content": "The pattern or generalization",
   553	    "source_ids": ["id1", "id2", "id3"],
   554	    "sources": ["evidence 1", "evidence 2"],
   555	    "pattern_type": "tendency",  // preference|behavior|personality|tendency|correlation
   556	    "confidence": "medium"  // low (2 sources), medium (3-4), high (5+)
   557	  }}]
   558	}}
   559	```
   560

>nl -ba src/crud/peer_card.py | sed -n '1,120p'

Chunk ID: 21b4da
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 941
Output:
     1	from __future__ import annotations
     2	
     3	import logging
     4	from typing import Any, cast
     5	
     6	from sqlalchemy import update
     7	from sqlalchemy.engine import CursorResult
     8	from sqlalchemy.ext.asyncio import AsyncSession
     9	
    10	from src import exceptions, models, schemas
    11	from src.cache.client import safe_cache_delete
    12	from src.crud.peer import get_or_create_peers, get_peer, peer_cache_key
    13	
    14	logger = logging.getLogger(__name__)
    15	
    16	
    17	async def get_peer_card(
    18	    db: AsyncSession,
    19	    workspace_name: str,
    20	    *,
    21	    observer: str,
    22	    observed: str,
    23	) -> list[str] | None:
    24	    """
    25	    Get peer card from internal_metadata.
    26	
    27	    The peer card is returned for the observer/observed relationship.
    28	
    29	    Args:
    30	        db: Database session
    31	        workspace_name: Name of the workspace
    32	        observed: Peer name of the peer described in the peer card
    33	        observer: Peer name of the observer
    34	
    35	    Returns:
    36	        The peer's card text if present, otherwise None.
    37	
    38	    Raises:
    39	        ResourceNotFoundException: If the peer does not exist.
    40	    """
    41	    peer = await get_peer(db, workspace_name, schemas.PeerCreate(name=observer))
    42	    return cast(
    43	        list[str] | None,
    44	        peer.internal_metadata.get(
    45	            construct_peer_card_label(observer=observer, observed=observed)
    46	        ),
    47	    )
    48	
    49	
    50	async def set_peer_card(
    51	    db: AsyncSession,
    52	    workspace_name: str,
    53	    peer_card: list[str],
    54	    *,
    55	    observer: str,
    56	    observed: str,
    57	) -> None:
    58	    """
    59	    Set peer card for a peer.
    60	
    61	    If observer_name is provided, the peer card is set for the observer/observed relationship.
    62	
    63	    Args:
    64	        db: Database session
    65	        workspace_name: Name of the workspace
    66	        peer_card: List of strings to set as the peer card
    67	        observed: Peer name of the peer described in the peer card
    68	        observer: Peer name of the observer
    69	
    70	    """
    71	    # Ensure the peer exists (get-or-create)
    72	    peers_result = await get_or_create_peers(
    73	        db, workspace_name, [schemas.PeerCreate(name=observer)]
    74	    )
    75	
    76	    stmt = (
    77	        update(models.Peer)
    78	        .where(models.Peer.workspace_name == workspace_name)
    79	        .where(models.Peer.name == observer)
    80	        .values(
    81	            internal_metadata=models.Peer.internal_metadata.op("||")(
    82	                {
    83	                    construct_peer_card_label(
    84	                        observer=observer, observed=observed
    85	                    ): peer_card
    86	                }
    87	            )
    88	        )
    89	    )
    90	    result = cast(CursorResult[Any], await db.execute(stmt))
    91	    if result.rowcount == 0:
    92	        raise exceptions.ResourceNotFoundException(
    93	            f"Peer {observer} not found in workspace {workspace_name}"
    94	        )
    95	    await db.commit()
    96	    await peers_result.post_commit()
    97	
    98	    # Invalidate cache - read-through pattern
    99	    cache_key = peer_cache_key(workspace_name, observer)
   100	    await safe_cache_delete(cache_key)
   101	
   102	
   103	def construct_peer_card_label(*, observer: str, observed: str) -> str:
   104	    if observer == observed:
   105	        return "peer_card"
   106	    return f"{observed}_peer_card"

>rg "MessageEmbedding|message_embeddings|embed_messages|create.*embedding|simple_batch_embed" src/crud src/reconciler src/routers src/deriver src/utils -g'*.py'

Chunk ID: f277ae
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1869
Output:
src/utils/agent_tools.py:        embeddings = await embedding_client.simple_batch_embed(contents)
src/utils/agent_tools.py:            "Batch embedding failed for create_observations; falling back to per-observation embedding: %s",
src/utils/agent_tools.py:        query_embeddings = await embedding_client.simple_batch_embed(semantic_queries)
src/crud/representation.py:            embeddings = await embedding_client.simple_batch_embed(observation_texts)
src/reconciler/sync_vectors.py:    message_embeddings_synced: int = 0
src/reconciler/sync_vectors.py:    message_embeddings_failed: int = 0
src/reconciler/sync_vectors.py:        return self.documents_synced + self.message_embeddings_synced
src/reconciler/sync_vectors.py:        return self.documents_failed + self.message_embeddings_failed
src/reconciler/sync_vectors.py:async def _get_message_embeddings_needing_sync(
src/reconciler/sync_vectors.py:) -> list[models.MessageEmbedding]:
src/reconciler/sync_vectors.py:        select(models.MessageEmbedding)
src/reconciler/sync_vectors.py:                models.MessageEmbedding.sync_state == "pending",
src/reconciler/sync_vectors.py:                _backoff_eligible(models.MessageEmbedding.last_sync_at),
src/reconciler/sync_vectors.py:        .order_by(models.MessageEmbedding.last_sync_at.asc().nullsfirst())
src/reconciler/sync_vectors.py:    embeddings: list[models.MessageEmbedding],
src/reconciler/sync_vectors.py:            update(models.MessageEmbedding)
src/reconciler/sync_vectors.py:            .where(models.MessageEmbedding.id == emb.id)
src/reconciler/sync_vectors.py:            new_embeddings = await embedding_client.simple_batch_embed(contents)
src/reconciler/sync_vectors.py:async def _sync_message_embeddings(
src/reconciler/sync_vectors.py:    embeddings: list[models.MessageEmbedding],
src/reconciler/sync_vectors.py:    embs_needing_embed: list[models.MessageEmbedding] = [
src/reconciler/sync_vectors.py:            new_embeddings = await embedding_client.simple_batch_embed(contents)
src/reconciler/sync_vectors.py:    failed_to_embed: list[models.MessageEmbedding] = [
src/reconciler/sync_vectors.py:    # TODO: chunk_position is computed from MessageEmbedding row ordering by ID, which is
src/reconciler/sync_vectors.py:    # fragile. If rows are deleted and re-created (e.g., during re-embedding), IDs change
src/reconciler/sync_vectors.py:    # 1. Persisting chunk_position in the MessageEmbedding table
src/reconciler/sync_vectors.py:    # 2. Removing MessageEmbedding table entirely if it becomes unnecessary
src/reconciler/sync_vectors.py:        select(models.MessageEmbedding.id, models.MessageEmbedding.message_id)
src/reconciler/sync_vectors.py:        .where(models.MessageEmbedding.message_id.in_(message_ids))
src/reconciler/sync_vectors.py:        .order_by(models.MessageEmbedding.message_id, models.MessageEmbedding.id)
src/reconciler/sync_vectors.py:    by_namespace: dict[str, list[models.MessageEmbedding]] = {}
src/reconciler/sync_vectors.py:        embs_to_sync: list[models.MessageEmbedding] = []
src/reconciler/sync_vectors.py:                update(models.MessageEmbedding)
src/reconciler/sync_vectors.py:                .where(models.MessageEmbedding.id.in_([e.id for e in embs_to_sync]))
src/reconciler/sync_vectors.py:async def _reconcile_message_embeddings_batch(
src/reconciler/sync_vectors.py:        embs = await _get_message_embeddings_needing_sync(db)
src/reconciler/sync_vectors.py:        synced, failed = await _sync_message_embeddings(db, embs, external_vector_store)
src/reconciler/sync_vectors.py:        metrics.message_embeddings_synced += synced
src/reconciler/sync_vectors.py:        metrics.message_embeddings_failed += failed
src/reconciler/sync_vectors.py:        embs_work = await _reconcile_message_embeddings_batch(
src/crud/workspace.py:            delete(models.MessageEmbedding).where(
src/crud/workspace.py:                models.MessageEmbedding.workspace_name == workspace_name
src/deriver/consumer.py:                metrics.message_embeddings_synced,
src/deriver/consumer.py:                metrics.message_embeddings_failed,
src/deriver/consumer.py:                    message_embeddings_synced=metrics.message_embeddings_synced,
src/deriver/consumer.py:                    message_embeddings_failed=metrics.message_embeddings_failed,
src/utils/search.py:    distance_expr = models.MessageEmbedding.embedding.cosine_distance(embedding_query)
src/utils/search.py:            models.MessageEmbedding,
src/utils/search.py:            models.Message.public_id == models.MessageEmbedding.message_id,
src/utils/search.py:        .where(models.MessageEmbedding.embedding.isnot(None))
src/utils/search.py:        .where(models.MessageEmbedding.workspace_name == workspace_name)
src/crud/session.py:        # Fetch all MessageEmbedding records to build vector IDs with {message_id}_{chunk_index}
src/crud/session.py:            select(models.MessageEmbedding).where(
src/crud/session.py:                models.MessageEmbedding.session_name == session_name,
src/crud/session.py:                models.MessageEmbedding.workspace_name == workspace_name,
src/crud/session.py:            message_chunks: dict[str, list[models.MessageEmbedding]] = {}
src/crud/session.py:        # Delete MessageEmbedding entries in batches
src/crud/session.py:            models.MessageEmbedding,
src/crud/session.py:                models.MessageEmbedding.session_name == session_name,
src/crud/session.py:                models.MessageEmbedding.workspace_name == workspace_name,
src/crud/document.py:        embeddings = await embedding_client.simple_batch_embed(contents)
src/crud/message.py:            # Create MessageEmbedding entries
src/crud/message.py:            embedding_objects: list[models.MessageEmbedding] = []
src/crud/message.py:                    embedding_obj = models.MessageEmbedding(
src/crud/message.py:            # Always create MessageEmbedding rows so reconciliation can track sync state
src/crud/message.py:                        update(models.MessageEmbedding)
src/crud/message.py:                        .where(models.MessageEmbedding.id.in_(embedding_ids))
src/crud/message.py:                                update(models.MessageEmbedding)
src/crud/message.py:                                .where(models.MessageEmbedding.id.in_(embedding_ids))
src/crud/message.py:                                update(models.MessageEmbedding)
src/crud/message.py:                                .where(models.MessageEmbedding.id.in_(embedding_ids))
src/crud/message.py:                                    sync_attempts=models.MessageEmbedding.sync_attempts
src/crud/message.py:                                update(models.MessageEmbedding)
src/crud/message.py:                                .where(models.MessageEmbedding.id.in_(embedding_ids))
src/crud/message.py:                                    sync_attempts=models.MessageEmbedding.sync_attempts
src/crud/message.py:            models.MessageEmbedding,
src/crud/message.py:            models.Message.public_id == models.MessageEmbedding.message_id,
src/crud/message.py:        .where(models.MessageEmbedding.workspace_name == workspace_name)
src/crud/message.py:        .order_by(models.MessageEmbedding.embedding.cosine_distance(query_embedding))
src/crud/message.py:            models.MessageEmbedding.session_name == session_name
src/crud/message.py:            models.MessageEmbedding.session_name.in_(allowed_session_names)

>nl -ba src/utils/agent_tools.py | sed -n '1120,1500p'

Chunk ID: 2962b7
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 4172
Output:
  1120	
  1121	    Returns:
  1122	        Dict with 'messages' list containing potentially relevant messages
  1123	    """
  1124	    messages: list[str] = []
  1125	    seen_content: set[str] = set()  # Dedupe by content hash
  1126	
  1127	    # Semantic queries to find preference-like content
  1128	    semantic_queries = [
  1129	        "user preferences and communication style",
  1130	        "standing instructions and rules to follow",
  1131	        "how user wants responses formatted",
  1132	        "user requirements and constraints",
  1133	        "things user wants or does not want",
  1134	    ]
  1135	
  1136	    # Batch embed all queries in a single API call (no DB needed).
  1137	    # If batching fails, each search call will generate its own embedding.
  1138	    query_embeddings_by_query: dict[str, list[float]] | None = None
  1139	    try:
  1140	        query_embeddings = await embedding_client.simple_batch_embed(semantic_queries)
  1141	        query_embeddings_by_query = dict(
  1142	            zip(semantic_queries, query_embeddings, strict=True)
  1143	        )
  1144	    except Exception as e:
  1145	        logger.warning(
  1146	            "Batch embedding failed for extract_preferences; falling back to per-query embedding in search_messages: %s",
  1147	            e,
  1148	        )
  1149	
  1150	    for query in semantic_queries:
  1151	        try:
  1152	            snippets = await crud.search_messages(
  1153	                workspace_name=workspace_name,
  1154	                session_name=session_name,
  1155	                query=query,
  1156	                limit=10,
  1157	                context_window=0,
  1158	                embedding=(
  1159	                    query_embeddings_by_query.get(query)
  1160	                    if query_embeddings_by_query is not None
  1161	                    else None
  1162	                ),
  1163	                observer=observer,
  1164	            )
  1165	            for matches, _ in snippets:
  1166	                for msg in matches:
  1167	                    if msg.peer_name == observed:
  1168	                        content_key = msg.content[:100].lower()
  1169	                        if content_key not in seen_content:
  1170	                            seen_content.add(content_key)
  1171	                            messages.append(f"'{msg.content.strip()}'")
  1172	        except Exception as e:
  1173	            logger.warning("Error in semantic search for '%s': %s", query, e)
  1174	
  1175	    return {
  1176	        "instructions": [],  # Deprecated - LLM will categorize
  1177	        "preferences": [],  # Deprecated - LLM will categorize
  1178	        "messages": messages[:30],  # Raw messages for LLM to process
  1179	    }
  1180	
  1181	
  1182	@dataclass
  1183	class ToolContext:
  1184	    """Context object passed to tool handlers."""
  1185	
  1186	    workspace_name: str
  1187	    observer: str
  1188	    observed: str
  1189	    session_name: str | None
  1190	    current_messages: list[models.Message] | None
  1191	    include_observation_ids: bool
  1192	    history_token_limit: int
  1193	    # Shared lock for serializing writes to the same workspace/observer/observed.
  1194	    # This lock is obtained from the module-level registry to ensure all concurrent
  1195	    # tool executors for the same data share the same lock.
  1196	    db_lock: asyncio.Lock
  1197	    # Optional resolved configuration for checking feature flags
  1198	    configuration: ResolvedConfiguration | None = None
  1199	    # Telemetry context fields
  1200	    run_id: str | None = None
  1201	    agent_type: str | None = None  # "dialectic", "deriver", "dreamer"
  1202	    parent_category: str | None = None  # Parent category for CloudEvents
  1203	
  1204	
  1205	async def _handle_create_observations_impl(
  1206	    ctx: ToolContext,
  1207	    tool_input: dict[str, Any],
  1208	    *,
  1209	    forced_level: str | None = None,
  1210	) -> str:
  1211	    """Handle create_observations tool."""
  1212	    raw_observations = tool_input.get("observations", [])
  1213	
  1214	    if not raw_observations:
  1215	        return "ERROR: observations list is empty"
  1216	
  1217	    # Set context-specific default level before Pydantic validation
  1218	    default_level = "explicit" if ctx.current_messages else "deductive"
  1219	    for obs in raw_observations:
  1220	        if forced_level is not None:
  1221	            obs["level"] = forced_level
  1222	        else:
  1223	            obs.setdefault("level", default_level)
  1224	
  1225	    # Validate observations individually so valid ones are still processed
  1226	    observations: list[schemas.ObservationInput] = []
  1227	    validation_failures: list[ObservationFailure] = []
  1228	    for obs in raw_observations:
  1229	        try:
  1230	            validated = schemas.ObservationInput.model_validate(obs)
  1231	        except ValidationError as e:
  1232	            validation_failures.append(
  1233	                ObservationFailure(
  1234	                    content_preview=str(obs.get("content", ""))[:50],
  1235	                    error=f"Validation failed: {e}",
  1236	                )
  1237	            )
  1238	            continue
  1239	        # Deriver can only create explicit observations
  1240	        if ctx.current_messages and validated.level != "explicit":
  1241	            validation_failures.append(
  1242	                ObservationFailure(
  1243	                    content_preview=validated.content[:50],
  1244	                    error=f"Deriver can only create 'explicit' observations, got '{validated.level}'",
  1245	                )
  1246	            )
  1247	            continue
  1248	        observations.append(validated)
  1249	
  1250	    if not observations:
  1251	        failure_details = "; ".join(
  1252	            f"'{f.content_preview}': {f.error}" for f in validation_failures
  1253	        )
  1254	        return f"ERROR: All observations failed validation: {failure_details}"
  1255	
  1256	    # Determine message context
  1257	    if ctx.current_messages:
  1258	        message_ids = [msg.id for msg in ctx.current_messages]
  1259	        message_created_at = str(ctx.current_messages[-1].created_at)
  1260	    else:
  1261	        message_ids = []
  1262	        message_created_at = utc_now_iso()
  1263	
  1264	    # Use lock to serialize database writes (prevents concurrent commit issues)
  1265	    async with ctx.db_lock:
  1266	        result = await create_observations(
  1267	            observations=observations,
  1268	            observer=ctx.observer,
  1269	            observed=ctx.observed,
  1270	            session_name=ctx.session_name,
  1271	            workspace_name=ctx.workspace_name,
  1272	            message_ids=message_ids,
  1273	            message_created_at=message_created_at,
  1274	        )
  1275	
  1276	    # Merge validation and embedding failures
  1277	    all_failures = validation_failures + result.failed
  1278	
  1279	    # Count levels from actually-created observations
  1280	    levels = result.created_levels
  1281	    explicit_count = levels.count("explicit")
  1282	    deductive_count = levels.count("deductive")
  1283	    inductive_count = levels.count("inductive")
  1284	    contradiction_count = levels.count("contradiction")
  1285	
  1286	    # Emit telemetry event if context is available
  1287	    if ctx.run_id and ctx.agent_type and ctx.parent_category:
  1288	        emit(
  1289	            AgentToolConclusionsCreatedEvent(
  1290	                run_id=ctx.run_id,
  1291	                iteration=get_current_iteration(),
  1292	                parent_category=ctx.parent_category,
  1293	                agent_type=ctx.agent_type,
  1294	                workspace_name=ctx.workspace_name,
  1295	                observer=ctx.observer,
  1296	                observed=ctx.observed,
  1297	                conclusion_count=result.created_count,
  1298	                levels=levels,
  1299	            )
  1300	        )
  1301	
  1302	    response = (
  1303	        f"Created {result.created_count} observations for {ctx.observed} by {ctx.observer} "
  1304	        f"({explicit_count} explicit, {deductive_count} deductive, "
  1305	        f"{inductive_count} inductive, {contradiction_count} contradiction)"
  1306	    )
  1307	
  1308	    if all_failures:
  1309	        failure_details = "; ".join(
  1310	            f"'{f.content_preview}': {f.error}" for f in all_failures
  1311	        )
  1312	        response += f"\nFailed {len(all_failures)}: {failure_details}"
  1313	
  1314	    return response
  1315	
  1316	
  1317	async def _handle_create_observations(
  1318	    ctx: ToolContext, tool_input: dict[str, Any]
  1319	) -> str:
  1320	    return await _handle_create_observations_impl(ctx, tool_input)
  1321	
  1322	
  1323	async def _handle_create_observations_deductive(
  1324	    ctx: ToolContext, tool_input: dict[str, Any]
  1325	) -> str:
  1326	    return await _handle_create_observations_impl(
  1327	        ctx,
  1328	        tool_input,
  1329	        forced_level="deductive",
  1330	    )
  1331	
  1332	
  1333	async def _handle_create_observations_inductive(
  1334	    ctx: ToolContext, tool_input: dict[str, Any]
  1335	) -> str:
  1336	    return await _handle_create_observations_impl(
  1337	        ctx,
  1338	        tool_input,
  1339	        forced_level="inductive",
  1340	    )
  1341	
  1342	
  1343	async def _handle_update_peer_card(ctx: ToolContext, tool_input: dict[str, Any]) -> str:
  1344	    """Handle update_peer_card tool."""
  1345	    # Check if peer card creation is disabled via configuration
  1346	    if ctx.configuration is not None and not ctx.configuration.peer_card.create:
  1347	        logger.info(
  1348	            "Peer card creation disabled for %s, skipping update",
  1349	            ctx.workspace_name,
  1350	        )
  1351	        return (
  1352	            "Peer card creation is disabled for this workspace/session configuration."
  1353	        )
  1354	
  1355	    raw_peer_card_content = tool_input.get("content")
  1356	
  1357	    # Guard against None or empty content — keep the existing peer card.
  1358	    if raw_peer_card_content is None:
  1359	        logger.warning(
  1360	            "Peer card update called with None content for %s, keeping existing card",
  1361	            ctx.workspace_name,
  1362	        )
  1363	        return "Peer card content was empty, no update performed."
  1364	
  1365	    # Normalize and deduplicate to keep peer cards bounded and stable.
  1366	    normalized_peer_card: list[str] = []
  1367	    seen: set[str] = set()
  1368	    items = (
  1369	        cast(list[str], raw_peer_card_content)
  1370	        if isinstance(raw_peer_card_content, list)
  1371	        else [str(raw_peer_card_content)]
  1372	    )
  1373	    for item in items:
  1374	        line = str(item).strip()
  1375	        if not line:
  1376	            continue
  1377	
  1378	        # Case-insensitive dedupe with whitespace normalization.
  1379	        normalized_key = " ".join(line.lower().split())
  1380	        if normalized_key in seen:
  1381	            continue
  1382	        seen.add(normalized_key)
  1383	        normalized_peer_card.append(line)
  1384	
  1385	    # Don't clear the peer card if all content normalized to empty.
  1386	    if not normalized_peer_card:
  1387	        logger.warning(
  1388	            "Peer card update normalized to empty for %s, keeping existing card",
  1389	            ctx.workspace_name,
  1390	        )
  1391	        return "Peer card content was empty after normalization, no update performed."
  1392	
  1393	    if len(normalized_peer_card) > MAX_PEER_CARD_FACTS:
  1394	        logger.warning(
  1395	            "Peer card update exceeded max facts (%s), truncating from %s to %s",
  1396	            MAX_PEER_CARD_FACTS,
  1397	            len(normalized_peer_card),
  1398	            MAX_PEER_CARD_FACTS,
  1399	        )
  1400	        normalized_peer_card = normalized_peer_card[:MAX_PEER_CARD_FACTS]
  1401	
  1402	    async with ctx.db_lock, tracked_db("tool.update_peer_card") as db:
  1403	        await crud.set_peer_card(
  1404	            db,
  1405	            workspace_name=ctx.workspace_name,
  1406	            peer_card=normalized_peer_card,
  1407	            observer=ctx.observer,
  1408	            observed=ctx.observed,
  1409	        )
  1410	    logger.info(
  1411	        f"Updated peer card for {ctx.workspace_name}/{ctx.observer}/{ctx.observed}"
  1412	    )
  1413	
  1414	    # Emit telemetry event if context is available
  1415	    if ctx.run_id and ctx.agent_type and ctx.parent_category:
  1416	        # Count facts in peer card (content is a list of strings per tool schema)
  1417	        emit(
  1418	            AgentToolPeerCardUpdatedEvent(
  1419	                run_id=ctx.run_id,
  1420	                iteration=get_current_iteration(),
  1421	                parent_category=ctx.parent_category,
  1422	                agent_type=ctx.agent_type,
  1423	                workspace_name=ctx.workspace_name,
  1424	                observer=ctx.observer,
  1425	                observed=ctx.observed,
  1426	                facts_count=len(normalized_peer_card),
  1427	            )
  1428	        )
  1429	
  1430	    return f"Updated peer card for {ctx.observed} by {ctx.observer}"
  1431	
  1432	
  1433	async def _handle_get_recent_history(
  1434	    ctx: ToolContext, tool_input: dict[str, Any]
  1435	) -> str:
  1436	    """Handle get_recent_history tool."""
  1437	    _ = tool_input
  1438	    async with tracked_db("tool.get_recent_history") as db:
  1439	        history: list[models.Message] = await get_recent_history(
  1440	            db,
  1441	            workspace_name=ctx.workspace_name,
  1442	            session_name=ctx.session_name,
  1443	            observed=ctx.observed,
  1444	            token_limit=[REDACTED]
  1445	        )
  1446	        if not history:
  1447	            return "No conversation history available"
  1448	        history_text = "\n".join(
  1449	            [f"{m.peer_name}: {_truncate_message_content(m.content)}" for m in history]
  1450	        )
  1451	    scope = (
  1452	        f"from session {ctx.session_name}"
  1453	        if ctx.session_name
  1454	        else f"from {ctx.observed} across sessions"
  1455	    )
  1456	    output = f"Conversation history ({len(history)} messages {scope}):\n{history_text}"
  1457	    return _truncate_tool_output(output)
  1458	
  1459	
  1460	async def _handle_search_memory(ctx: ToolContext, tool_input: dict[str, Any]) -> str:
  1461	    """Handle search_memory tool."""
  1462	    top_k = min(_safe_int(tool_input.get("top_k"), 20), 40)
  1463	    query = tool_input["query"]
  1464	    try:
  1465	        query_embedding = await embedding_client.embed(query)
  1466	    except ValueError:
  1467	        return (
  1468	            "ERROR: Query exceeds maximum token limit of "
  1469	            + f"{settings.EMBEDDING.MAX_INPUT_TOKENS}. Please use a shorter query."
  1470	        )
  1471	
  1472	    documents = await crud.query_documents(
  1473	        db=None,
  1474	        workspace_name=ctx.workspace_name,
  1475	        observer=ctx.observer,
  1476	        observed=ctx.observed,
  1477	        query=query,
  1478	        top_k=top_k,
  1479	        embedding=query_embedding,
  1480	    )
  1481	    mem = Representation.from_documents(documents)
  1482	    total_count = mem.len()
  1483	    if total_count == 0:
  1484	        # fallback behavior: if the memory is *empty*, that means we're quite
  1485	        # early in a workspace/peer/session -- in order to give good answers in
  1486	        # this stage, and be efficient with tool calls, and make sure the model
  1487	        # doesn't short-circuit and think there's nothing here, we
  1488	        # automatically search the message history for relevant information.
  1489	        if ctx.agent_type == "dialectic":
  1490	            limit = min(_safe_int(tool_input.get("top_k"), 20), 20)
  1491	            message_output = None
  1492	            snippets = await crud.search_messages(
  1493	                workspace_name=ctx.workspace_name,
  1494	                session_name=ctx.session_name,
  1495	                query=query,
  1496	                limit=limit,
  1497	                context_window=0,
  1498	                embedding=query_embedding,
  1499	                observer=ctx.observer,
  1500	            )

>nl -ba src/crud/message.py | sed -n '1,260p'

Chunk ID: 95ee10
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2734
Output:
     1	from collections.abc import Sequence
     2	from datetime import datetime
     3	from logging import getLogger
     4	from typing import Any
     5	
     6	from nanoid import generate as generate_nanoid
     7	from sqlalchemy import ColumnElement, Select, and_, func, or_, select, text, update
     8	from sqlalchemy.ext.asyncio import AsyncSession
     9	
    10	from src import models, schemas
    11	from src.config import settings
    12	from src.dependencies import tracked_db
    13	from src.embedding_client import embedding_client
    14	from src.exceptions import VectorStoreError
    15	from src.utils.filter import apply_filter
    16	from src.utils.formatting import ILIKE_ESCAPE_CHAR, escape_ilike_pattern
    17	from src.vector_store import VectorRecord, get_external_vector_store
    18	
    19	from .session import get_or_create_session
    20	
    21	logger = getLogger(__name__)
    22	
    23	
    24	def _deduplicate_messages(
    25	    messages: Sequence[models.Message], limit: int
    26	) -> list[models.Message]:
    27	    """Deduplicate messages by public_id, preserving input order."""
    28	    seen: set[str] = set()
    29	    result: list[models.Message] = []
    30	    for msg in messages:
    31	        if msg.public_id not in seen:
    32	            seen.add(msg.public_id)
    33	            result.append(msg)
    34	            if len(result) >= limit:
    35	                break
    36	    return result
    37	
    38	
    39	def _expunge_snippets(
    40	    db: AsyncSession, snippets: list[tuple[list[models.Message], list[models.Message]]]
    41	) -> None:
    42	    """Detach snippet messages from the session, guarding against duplicates."""
    43	    seen: set[int] = set()
    44	    for matches, context in snippets:
    45	        for msg in [*matches, *context]:
    46	            obj_id = id(msg)
    47	            if obj_id in seen:
    48	                continue
    49	            db.expunge(msg)
    50	            seen.add(obj_id)
    51	
    52	
    53	async def get_peer_session_names(
    54	    db: AsyncSession,
    55	    workspace_name: str,
    56	    peer_name: str,
    57	) -> list[str]:
    58	    """Get all session names where a peer has any membership record.
    59	
    60	    Any membership record (regardless of joined_at/left_at) grants visibility
    61	    to all messages in that session.
    62	    """
    63	    stmt = (
    64	        select(models.session_peers_table.c.session_name)
    65	        .where(models.session_peers_table.c.workspace_name == workspace_name)
    66	        .where(models.session_peers_table.c.peer_name == peer_name)
    67	        .distinct()
    68	    )
    69	    result = await db.execute(stmt)
    70	    return [row[0] for row in result.all()]
    71	
    72	
    73	def _apply_token_limit(
    74	    base_conditions: list[ColumnElement[Any]], token_limit: int
    75	) -> Select[tuple[models.Message]]:
    76	    """
    77	    Helper function to apply token limit logic to a message query.
    78	
    79	    Creates a subquery that calculates running sum of tokens for most recent messages
    80	    and returns a select statement that joins with this subquery to limit results
    81	    based on token count.
    82	
    83	    Args:
    84	        base_conditions: List of conditions to apply to the base query
    85	        token_limit: Maximum number of tokens to include in the messages
    86	
    87	    Returns:
    88	        Select statement with token limit applied
    89	    """
    90	    # Create a subquery that calculates running sum of tokens for most recent messages
    91	    token_subquery = (
    92	        select(
    93	            models.Message.id,
    94	            func.sum(models.Message.token_count)
    95	            .over(order_by=models.Message.id.desc())
    96	            .label("running_token_sum"),
    97	        )
    98	        .where(*base_conditions)
    99	        .subquery()
   100	    )
   101	
   102	    # Select Message objects where running sum doesn't exceed token_limit
   103	    return (
   104	        select(models.Message)
   105	        .join(token_subquery, models.Message.id == token_subquery.c.id)
   106	        .where(token_subquery.c.running_token_sum <= token_limit)
   107	    )
   108	
   109	
   110	async def _build_merged_snippets(
   111	    db: AsyncSession,
   112	    workspace_name: str,
   113	    matched_messages: list[models.Message],
   114	    context_window: int,
   115	) -> list[tuple[list[models.Message], list[models.Message]]]:
   116	    """
   117	    Group matched messages by session, merge overlapping context ranges, and fetch context.
   118	
   119	    Takes a list of matched messages and builds conversation snippets by:
   120	    1. Grouping matches by session name
   121	    2. Sorting matches within each session by sequence number
   122	    3. Merging overlapping context windows to avoid duplicate context
   123	    4. Fetching the full context for each merged range from the database
   124	
   125	    Args:
   126	        db: Database session
   127	        workspace_name: Name of the workspace
   128	        matched_messages: List of messages that matched a search query
   129	        context_window: Number of messages before/after each match to include
   130	
   131	    Returns:
   132	        List of tuples: (matched_messages_in_range, context_messages)
   133	        Each tuple represents a snippet where context_messages includes all messages
   134	        in the merged range (including the matched messages), ordered chronologically.
   135	    """
   136	    if not matched_messages:
   137	        return []
   138	
   139	    session_matches: dict[str, list[models.Message]] = {}
   140	    for msg in matched_messages:
   141	        session_matches.setdefault(msg.session_name, []).append(msg)
   142	
   143	    # Build merged ranges per session, then issue a single batched query
   144	    session_ranges: dict[str, list[tuple[int, int, list[models.Message]]]] = {}
   145	    for sess_name, matches in session_matches.items():
   146	        matches.sort(key=lambda m: m.seq_in_session)
   147	
   148	        merged_ranges: list[tuple[int, int, list[models.Message]]] = []
   149	        for match in matches:
   150	            start = match.seq_in_session - context_window
   151	            end = match.seq_in_session + context_window
   152	
   153	            if merged_ranges and start <= merged_ranges[-1][1] + 1:
   154	                prev_start, prev_end, prev_matches = merged_ranges[-1]
   155	                merged_ranges[-1] = (
   156	                    prev_start,
   157	                    max(prev_end, end),
   158	                    [*prev_matches, match],
   159	                )
   160	            else:
   161	                merged_ranges.append((start, end, [match]))
   162	
   163	        session_ranges[sess_name] = merged_ranges
   164	
   165	    # One OR-of-ANDs predicate covers every (session, range) pair
   166	    session_predicates = [
   167	        and_(
   168	            models.Message.session_name == sess_name,
   169	            or_(
   170	                *(
   171	                    models.Message.seq_in_session.between(start_seq, end_seq)
   172	                    for start_seq, end_seq, _ in merged_ranges
   173	                )
   174	            ),
   175	        )
   176	        for sess_name, merged_ranges in session_ranges.items()
   177	    ]
   178	
   179	    context_stmt = (
   180	        select(models.Message)
   181	        .where(models.Message.workspace_name == workspace_name)
   182	        .where(or_(*session_predicates))
   183	        .order_by(
   184	            models.Message.session_name.asc(),
   185	            models.Message.seq_in_session.asc(),
   186	        )
   187	    )
   188	
   189	    context_result = await db.execute(context_stmt)
   190	    by_session: dict[str, list[models.Message]] = {}
   191	    for msg in context_result.scalars().all():
   192	        by_session.setdefault(msg.session_name, []).append(msg)
   193	
   194	    snippets: list[
   195	        tuple[list[models.Message], list[models.Message]]
   196	    ] = []  # list of tuples, each containing query matches and context messages
   197	    for sess_name, merged_ranges in session_ranges.items():
   198	        all_context_messages = by_session.get(sess_name, [])
   199	        for start_seq, end_seq, range_matches in merged_ranges:
   200	            context_messages = [
   201	                msg
   202	                for msg in all_context_messages
   203	                if start_seq <= msg.seq_in_session <= end_seq
   204	            ]
   205	            snippets.append((range_matches, context_messages))
   206	
   207	    return snippets
   208	
   209	
   210	async def create_messages(
   211	    db: AsyncSession,
   212	    messages: list[schemas.MessageCreate],
   213	    workspace_name: str,
   214	    session_name: str,
   215	) -> list[models.Message]:
   216	    """
   217	    Bulk create messages for a session while maintaining order.
   218	
   219	    Args:
   220	        db: Database session
   221	        messages: List of messages to create
   222	        workspace_name: Name of the workspace
   223	        session_name: Name of the session to create messages in
   224	
   225	    Returns:
   226	        List of created message objects
   227	    """
   228	    # Get or create session with peers in messages list
   229	    peers = {message.peer_name: schemas.SessionPeerConfig() for message in messages}
   230	    await get_or_create_session(
   231	        db,
   232	        session=schemas.SessionCreate(name=session_name, peers=peers),
   233	        workspace_name=workspace_name,
   234	    )
   235	
   236	    await db.execute(text("SET LOCAL lock_timeout = '5s'"))
   237	    await db.execute(
   238	        text(
   239	            "SELECT pg_advisory_xact_lock(hashtext(:workspace_name), hashtext(:session_name))"
   240	        ),
   241	        {"workspace_name": workspace_name, "session_name": session_name},
   242	    )
   243	
   244	    # Get the last sequence number on a session - uses (workspace_name, session_name, seq_in_session) index
   245	    last_seq = (
   246	        await db.scalar(
   247	            select(models.Message.seq_in_session)
   248	            .where(
   249	                models.Message.workspace_name == workspace_name,
   250	                models.Message.session_name == session_name,
   251	            )
   252	            .order_by(models.Message.seq_in_session.desc())
   253	            .limit(1)
   254	        )
   255	        or 0
   256	    )
   257	
   258	    # Create list of message objects (this will trigger the before_insert event)
   259	    message_objects: list[models.Message] = []
   260	    for offset, message in enumerate(messages, start=1):

>nl -ba src/crud/message.py | sed -n '260,430p'

Chunk ID: e86182
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 2196
Output:
   260	    for offset, message in enumerate(messages, start=1):
   261	        message_seq_in_session = last_seq + offset
   262	        message_obj = models.Message(
   263	            session_name=session_name,
   264	            peer_name=message.peer_name,
   265	            content=message.content,
   266	            h_metadata=message.metadata or {},
   267	            workspace_name=workspace_name,
   268	            public_id=generate_nanoid(),
   269	            token_count=[REDACTED]
   270	            created_at=message.created_at,  # Use provided created_at if available
   271	            seq_in_session=message_seq_in_session,
   272	        )
   273	        message_objects.append(message_obj)
   274	
   275	    db.add_all(message_objects)
   276	
   277	    # Commit here to release the advisory lock before generating embeddings
   278	    await db.commit()
   279	    try:
   280	        if settings.EMBED_MESSAGES:
   281	            id_resource_dict = {
   282	                message.public_id: message.content
   283	                for message in message_objects
   284	                if message.content and message.content.strip()
   285	            }
   286	            embedding_dict = (
   287	                await embedding_client.batch_embed(id_resource_dict)
   288	                if id_resource_dict
   289	                else {}
   290	            )
   291	
   292	            external_vector_store = get_external_vector_store()
   293	
   294	            # Determine if we need to persist embeddings to postgres
   295	            # True when: TYPE=pgvector OR still migrating (dual-write to both stores)
   296	            store_embeddings_in_postgres = (
   297	                settings.VECTOR_STORE.TYPE == "pgvector"
   298	                or not settings.VECTOR_STORE.MIGRATED
   299	            )
   300	
   301	            # Create MessageEmbedding entries
   302	            embedding_objects: list[models.MessageEmbedding] = []
   303	            # Maps emb index -> (chunk_position, embedding vector)
   304	            pending_embedding_data: dict[int, tuple[int, list[float]]] = {}
   305	            for message_obj in message_objects:
   306	                embeddings = embedding_dict.get(message_obj.public_id, [])
   307	                for chunk_position, embedding in enumerate(embeddings):
   308	                    embedding_obj = models.MessageEmbedding(
   309	                        content=message_obj.content,
   310	                        message_id=message_obj.public_id,
   311	                        workspace_name=workspace_name,
   312	                        session_name=session_name,
   313	                        peer_name=message_obj.peer_name,
   314	                        sync_state="pending",
   315	                        embedding=embedding if store_embeddings_in_postgres else None,
   316	                    )
   317	                    emb_idx = len(embedding_objects)
   318	                    pending_embedding_data[emb_idx] = (chunk_position, embedding)
   319	                    embedding_objects.append(embedding_obj)
   320	
   321	            # Always create MessageEmbedding rows so reconciliation can track sync state
   322	            # even when embeddings aren't stored in postgres
   323	            embedding_ids: list[int] = []
   324	            if embedding_objects:
   325	                db.add_all(embedding_objects)
   326	                await db.flush()
   327	                embedding_ids = [emb.id for emb in embedding_objects]
   328	
   329	            await db.commit()
   330	
   331	            # If no external vector store (pgvector-only mode), mark as synced immediately
   332	            if external_vector_store is None:
   333	                if embedding_ids:
   334	                    await db.execute(
   335	                        update(models.MessageEmbedding)
   336	                        .where(models.MessageEmbedding.id.in_(embedding_ids))
   337	                        .values(
   338	                            sync_state="synced",
   339	                            last_sync_at=func.now(),
   340	                            sync_attempts=0,
   341	                        )
   342	                    )
   343	                    await db.commit()
   344	            else:
   345	                # External vector store - build and upsert vector records
   346	                namespace = external_vector_store.get_vector_namespace(
   347	                    "message", workspace_name
   348	                )
   349	
   350	                # Build vector records with {message_id}_{chunk_position} as vector ID
   351	                vector_records: list[VectorRecord] = []
   352	                for emb_idx, emb in enumerate(embedding_objects):
   353	                    chunk_position, embedding = pending_embedding_data[emb_idx]
   354	                    vector_id = f"{emb.message_id}_{chunk_position}"
   355	                    vector_records.append(
   356	                        VectorRecord(
   357	                            id=vector_id,
   358	                            embedding=list(embedding),
   359	                            metadata={
   360	                                "message_id": emb.message_id,
   361	                                "session_name": emb.session_name,
   362	                                "peer_name": emb.peer_name,
   363	                            },
   364	                        )
   365	                    )
   366	
   367	                # Upsert to external vector store and update sync state
   368	                if vector_records:
   369	                    try:
   370	                        await external_vector_store.upsert_many(
   371	                            namespace, vector_records
   372	                        )
   373	                        # Success: mark as synced if we have DB rows
   374	                        if embedding_ids:
   375	                            await db.execute(
   376	                                update(models.MessageEmbedding)
   377	                                .where(models.MessageEmbedding.id.in_(embedding_ids))
   378	                                .values(
   379	                                    sync_state="synced",
   380	                                    last_sync_at=func.now(),
   381	                                    sync_attempts=0,
   382	                                )
   383	                            )
   384	                            await db.commit()
   385	
   386	                    except VectorStoreError:
   387	                        logger.warning(
   388	                            "Vector store unavailable; leaving message vectors unsynced"
   389	                        )
   390	                        if embedding_ids:
   391	                            await db.execute(
   392	                                update(models.MessageEmbedding)
   393	                                .where(models.MessageEmbedding.id.in_(embedding_ids))
   394	                                .values(
   395	                                    sync_attempts=models.MessageEmbedding.sync_attempts
   396	                                    + 1,
   397	                                    last_sync_at=func.now(),
   398	                                )
   399	                            )
   400	                            await db.commit()
   401	
   402	                    except Exception:
   403	                        logger.exception("Unexpected error upserting message vectors")
   404	                        if embedding_ids:
   405	                            await db.execute(
   406	                                update(models.MessageEmbedding)
   407	                                .where(models.MessageEmbedding.id.in_(embedding_ids))
   408	                                .values(
   409	                                    sync_attempts=models.MessageEmbedding.sync_attempts
   410	                                    + 1,
   411	                                    last_sync_at=func.now(),
   412	                                )
   413	                            )
   414	                            await db.commit()
   415	
   416	    except Exception:
   417	        logger.exception(
   418	            "Failed to generate message embeddings for %s messages in workspace %s and session %s.",
   419	            len(message_objects),
   420	            workspace_name,
   421	            session_name,
   422	        )
   423	
   424	    return message_objects
   425	
   426	
   427	async def get_messages(
   428	    workspace_name: str,
   429	    session_name: str,
   430	    reverse: bool | None = False,

Honcho’s core model is a pairwise memory system layered on top of ordinary chat data.

At the bottom:

Workspace: tenant/container for everything.
Peer: any participant, human or agent. Peers are unique by (workspace_name, name).
Session: a conversation context inside a workspace.
session_peers: many-to-many membership table, with per-peer session config like observe_me / observe_others.
Message: raw utterance from a peer in a session, ordered by seq_in_session.
MessageEmbedding: vector-search rows for raw messages, used when agents need to search original conversation text.

See src/models.py, src/models.py, src/models.py, src/models.py, src/models.py, and src/models.py.

The memory layer is where Honcho becomes unusual:

Collection: one memory collection per (workspace, observer, observed).
Document: one persisted observation/conclusion inside that pairwise collection.
Document.level: explicit, deductive, inductive, or contradiction.
Document.source_ids: links higher-level observations back to source observations, giving Honcho a traversable reasoning graph.
Document.embedding: vector index for semantic memory search.

That means Alice’s model of Bob is separate from Bob’s model of Bob, and separate from Carol’s model of Bob. The schema enforces that with the observer, observed, and workspace_name keys on collections and documents; see src/models.py and src/models.py.

A Representation is the in-memory view over those documents. It is not a table. It groups observations into four levels:

explicit: facts directly stated.
deductive: logical implications.
inductive: patterns/generalizations.
contradiction: conflicting claims.

That shape is defined in src/utils/representation.py.

Message Flow When a message is created, Honcho stores the raw message, optionally embeds it, then enqueues background work. Message creation creates MessageEmbedding rows if EMBED_MESSAGES is enabled; see src/crud/message.py.

Then enqueueing decides what inference tasks are needed:

create summaries on configured message intervals,
create representation tasks if reasoning is enabled,
decide who observes whom based on observe_me and observe_others.

That logic is in src/deriver/enqueue.py. A message from peer observed may produce a self-observation collection (observed -> observed) and also other peer-perspective collections like (assistant -> observed) if session config says those peers observe others.

What Inference Is For Honcho uses inference for several distinct jobs because each job produces a different kind of artifact.

Explicit observation extraction

Raw messages are unstructured. The deriver asks an LLM to turn recent messages into structured explicit facts. It uses a structured PromptRepresentation whose only field is explicit, then saves those observations into every relevant observer/observed collection. See src/deriver/deriver.py and src/deriver/deriver.py.

This is inference, but constrained: “facts literally stated,” not broader reasoning.

Embeddings for retrieval

Honcho embeds messages and observations so agents can do semantic search over raw conversation and memory. This is not “reasoning” in the same sense as the LLM deriver, but it is still model inference. Observation embeddings are created when representations are saved; see src/crud/representation.py.

Session summaries

Summaries are stored in Session.internal_metadata, not as first-class rows. Honcho creates short and long rolling summaries to keep session context bounded while preserving key facts. The prompts explicitly ask for factual summaries, preferences, context, and, for long summaries, emotional state/personality themes. See src/utils/summarizer.py and src/utils/summarizer.py.

Dreaming / consolidation

The Dreamer runs later over accumulated observations. It has two specialist agents:

deduction: logical implications, updates, contradictions, outdated-observation deletion, peer-card updates;
induction: recurring patterns, preferences, traits, behavioral tendencies.

The orchestrator runs deduction then induction; see src/dreamer/orchestrator.py. The specialist prompts make the facets explicit in src/dreamer/specialists.py and src/dreamer/specialists.py.

Peer cards

A peer card is a compact durable profile stored on the observing peer’s internal_metadata, keyed by the observed peer. For self-observation it uses peer_card; for another peer it uses {observed}_peer_card. See src/crud/peer_card.py and src/crud/peer_card.py.

This is another “facet” because it is not the full memory graph. It is a small, durable profile cache: name, preferences, stable traits, standing instructions.

Dialectic answers

The Dialectic API is inference at query time. It prefetches semantically relevant explicit and derived observations, injects recent session history, then lets an LLM use tools to search memory/messages and synthesize an answer. See src/dialectic/core.py and src/dialectic/core.py.

So the short answer is: Honcho needs inference because its product is not “chat storage.” It is trying to maintain a layered, perspective-specific model of people. The different inference requests correspond to different compression/abstraction layers: raw messages -> explicit observations -> deductions/contradictions -> inductive patterns -> peer cards -> query-time dialectic answers.

I would like to visualize this

Here are two useful views.

Data Model

Inference Flow

Mental Model

The key visual idea: Message is the event log, Document is the memory graph, and (observer, observed) is the lens that keeps one peer’s model of another peer separate from everyone else’s.