forked from cardosofelipe/fast-next-template
Felipe Cardoso
3554efe66a
Implements the episodic memory service for storing and retrieving agent task execution experiences. This enables learning from past successes and failures. Components: - EpisodicMemory: Main service class combining recording and retrieval - EpisodeRecorder: Handles episode creation, importance scoring - EpisodeRetriever: Multiple retrieval strategies (recency, semantic, outcome, importance, task type) Key features: - Records task completions with context, actions, outcomes - Calculates importance scores based on outcome, duration, lessons - Semantic search with fallback to recency when embeddings unavailable - Full CRUD operations with statistics and summarization - Comprehensive unit tests (50 tests, all passing) Closes #90 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
504 lines
17 KiB
Python
504 lines
17 KiB
Python
# app/services/memory/episodic/retrieval.py
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"""
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Episode Retrieval Strategies.
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Provides different retrieval strategies for finding relevant episodes:
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- Semantic similarity (vector search)
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- Recency-based
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- Outcome-based filtering
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- Importance-based ranking
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"""
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import logging
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import time
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from abc import ABC, abstractmethod
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from datetime import datetime
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from enum import Enum
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from typing import Any
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from uuid import UUID
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from sqlalchemy import and_, desc, select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.models.memory.enums import EpisodeOutcome
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from app.models.memory.episode import Episode as EpisodeModel
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from app.services.memory.types import Episode, Outcome, RetrievalResult
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logger = logging.getLogger(__name__)
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class RetrievalStrategy(str, Enum):
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"""Retrieval strategy types."""
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SEMANTIC = "semantic"
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RECENCY = "recency"
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OUTCOME = "outcome"
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IMPORTANCE = "importance"
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HYBRID = "hybrid"
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def _model_to_episode(model: EpisodeModel) -> Episode:
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"""Convert SQLAlchemy model to Episode dataclass."""
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# SQLAlchemy Column types are inferred as Column[T] by mypy, but at runtime
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# they return actual values. We use type: ignore to handle this mismatch.
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return Episode(
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id=model.id, # type: ignore[arg-type]
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project_id=model.project_id, # type: ignore[arg-type]
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agent_instance_id=model.agent_instance_id, # type: ignore[arg-type]
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agent_type_id=model.agent_type_id, # type: ignore[arg-type]
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session_id=model.session_id, # type: ignore[arg-type]
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task_type=model.task_type, # type: ignore[arg-type]
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task_description=model.task_description, # type: ignore[arg-type]
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actions=model.actions or [], # type: ignore[arg-type]
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context_summary=model.context_summary, # type: ignore[arg-type]
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outcome=Outcome(model.outcome.value),
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outcome_details=model.outcome_details or "", # type: ignore[arg-type]
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duration_seconds=model.duration_seconds, # type: ignore[arg-type]
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tokens_used=model.tokens_used, # type: ignore[arg-type]
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lessons_learned=model.lessons_learned or [], # type: ignore[arg-type]
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importance_score=model.importance_score, # type: ignore[arg-type]
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embedding=None, # Don't expose raw embedding
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occurred_at=model.occurred_at, # type: ignore[arg-type]
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created_at=model.created_at, # type: ignore[arg-type]
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updated_at=model.updated_at, # type: ignore[arg-type]
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)
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class BaseRetriever(ABC):
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"""Abstract base class for episode retrieval strategies."""
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@abstractmethod
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async def retrieve(
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self,
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session: AsyncSession,
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project_id: UUID,
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limit: int = 10,
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**kwargs: Any,
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) -> RetrievalResult[Episode]:
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"""Retrieve episodes based on the strategy."""
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...
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class RecencyRetriever(BaseRetriever):
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"""Retrieves episodes by recency (most recent first)."""
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async def retrieve(
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self,
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session: AsyncSession,
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project_id: UUID,
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limit: int = 10,
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*,
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since: datetime | None = None,
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agent_instance_id: UUID | None = None,
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**kwargs: Any,
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) -> RetrievalResult[Episode]:
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"""Retrieve most recent episodes."""
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start_time = time.perf_counter()
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query = (
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select(EpisodeModel)
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.where(EpisodeModel.project_id == project_id)
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.order_by(desc(EpisodeModel.occurred_at))
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.limit(limit)
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)
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if since is not None:
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query = query.where(EpisodeModel.occurred_at >= since)
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if agent_instance_id is not None:
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query = query.where(EpisodeModel.agent_instance_id == agent_instance_id)
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result = await session.execute(query)
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models = list(result.scalars().all())
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# Get total count
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count_query = select(EpisodeModel).where(EpisodeModel.project_id == project_id)
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if since is not None:
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count_query = count_query.where(EpisodeModel.occurred_at >= since)
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count_result = await session.execute(count_query)
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total_count = len(list(count_result.scalars().all()))
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latency_ms = (time.perf_counter() - start_time) * 1000
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return RetrievalResult(
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items=[_model_to_episode(m) for m in models],
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total_count=total_count,
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query="recency",
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retrieval_type=RetrievalStrategy.RECENCY.value,
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latency_ms=latency_ms,
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metadata={"since": since.isoformat() if since else None},
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)
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class OutcomeRetriever(BaseRetriever):
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"""Retrieves episodes filtered by outcome."""
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async def retrieve(
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self,
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session: AsyncSession,
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project_id: UUID,
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limit: int = 10,
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*,
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outcome: Outcome | None = None,
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agent_instance_id: UUID | None = None,
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**kwargs: Any,
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) -> RetrievalResult[Episode]:
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"""Retrieve episodes by outcome."""
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start_time = time.perf_counter()
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query = (
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select(EpisodeModel)
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.where(EpisodeModel.project_id == project_id)
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.order_by(desc(EpisodeModel.occurred_at))
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.limit(limit)
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)
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if outcome is not None:
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db_outcome = EpisodeOutcome(outcome.value)
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query = query.where(EpisodeModel.outcome == db_outcome)
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if agent_instance_id is not None:
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query = query.where(EpisodeModel.agent_instance_id == agent_instance_id)
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result = await session.execute(query)
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models = list(result.scalars().all())
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# Get total count
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count_query = select(EpisodeModel).where(EpisodeModel.project_id == project_id)
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if outcome is not None:
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count_query = count_query.where(
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EpisodeModel.outcome == EpisodeOutcome(outcome.value)
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)
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count_result = await session.execute(count_query)
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total_count = len(list(count_result.scalars().all()))
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latency_ms = (time.perf_counter() - start_time) * 1000
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return RetrievalResult(
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items=[_model_to_episode(m) for m in models],
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total_count=total_count,
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query=f"outcome:{outcome.value if outcome else 'all'}",
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retrieval_type=RetrievalStrategy.OUTCOME.value,
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latency_ms=latency_ms,
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metadata={"outcome": outcome.value if outcome else None},
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)
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class TaskTypeRetriever(BaseRetriever):
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"""Retrieves episodes filtered by task type."""
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async def retrieve(
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self,
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session: AsyncSession,
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project_id: UUID,
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limit: int = 10,
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*,
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task_type: str | None = None,
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agent_instance_id: UUID | None = None,
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**kwargs: Any,
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) -> RetrievalResult[Episode]:
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"""Retrieve episodes by task type."""
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start_time = time.perf_counter()
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query = (
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select(EpisodeModel)
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.where(EpisodeModel.project_id == project_id)
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.order_by(desc(EpisodeModel.occurred_at))
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.limit(limit)
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)
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if task_type is not None:
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query = query.where(EpisodeModel.task_type == task_type)
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if agent_instance_id is not None:
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query = query.where(EpisodeModel.agent_instance_id == agent_instance_id)
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result = await session.execute(query)
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models = list(result.scalars().all())
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# Get total count
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count_query = select(EpisodeModel).where(EpisodeModel.project_id == project_id)
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if task_type is not None:
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count_query = count_query.where(EpisodeModel.task_type == task_type)
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count_result = await session.execute(count_query)
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total_count = len(list(count_result.scalars().all()))
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latency_ms = (time.perf_counter() - start_time) * 1000
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return RetrievalResult(
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items=[_model_to_episode(m) for m in models],
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total_count=total_count,
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query=f"task_type:{task_type or 'all'}",
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retrieval_type="task_type",
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latency_ms=latency_ms,
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metadata={"task_type": task_type},
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)
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class ImportanceRetriever(BaseRetriever):
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"""Retrieves episodes ranked by importance score."""
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async def retrieve(
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self,
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session: AsyncSession,
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project_id: UUID,
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limit: int = 10,
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*,
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min_importance: float = 0.0,
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agent_instance_id: UUID | None = None,
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**kwargs: Any,
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) -> RetrievalResult[Episode]:
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"""Retrieve episodes by importance."""
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start_time = time.perf_counter()
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query = (
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select(EpisodeModel)
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.where(
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and_(
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EpisodeModel.project_id == project_id,
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EpisodeModel.importance_score >= min_importance,
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)
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)
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.order_by(desc(EpisodeModel.importance_score))
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.limit(limit)
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)
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if agent_instance_id is not None:
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query = query.where(EpisodeModel.agent_instance_id == agent_instance_id)
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result = await session.execute(query)
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models = list(result.scalars().all())
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# Get total count
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count_query = select(EpisodeModel).where(
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and_(
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EpisodeModel.project_id == project_id,
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EpisodeModel.importance_score >= min_importance,
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)
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)
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count_result = await session.execute(count_query)
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total_count = len(list(count_result.scalars().all()))
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latency_ms = (time.perf_counter() - start_time) * 1000
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return RetrievalResult(
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items=[_model_to_episode(m) for m in models],
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total_count=total_count,
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query=f"importance>={min_importance}",
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retrieval_type=RetrievalStrategy.IMPORTANCE.value,
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latency_ms=latency_ms,
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metadata={"min_importance": min_importance},
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)
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class SemanticRetriever(BaseRetriever):
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"""Retrieves episodes by semantic similarity using vector search."""
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def __init__(self, embedding_generator: Any | None = None) -> None:
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"""Initialize with optional embedding generator."""
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self._embedding_generator = embedding_generator
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async def retrieve(
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self,
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session: AsyncSession,
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project_id: UUID,
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limit: int = 10,
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*,
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query_text: str | None = None,
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query_embedding: list[float] | None = None,
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agent_instance_id: UUID | None = None,
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**kwargs: Any,
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) -> RetrievalResult[Episode]:
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"""Retrieve episodes by semantic similarity."""
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start_time = time.perf_counter()
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# If no embedding provided, fall back to recency
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if query_embedding is None and query_text is None:
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logger.warning(
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"No query provided for semantic search, falling back to recency"
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)
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recency = RecencyRetriever()
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fallback_result = await recency.retrieve(
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session, project_id, limit, agent_instance_id=agent_instance_id
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)
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latency_ms = (time.perf_counter() - start_time) * 1000
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return RetrievalResult(
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items=fallback_result.items,
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total_count=fallback_result.total_count,
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query="no_query",
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retrieval_type=RetrievalStrategy.SEMANTIC.value,
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latency_ms=latency_ms,
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metadata={"fallback": "recency", "reason": "no_query"},
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)
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# Generate embedding if needed
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embedding = query_embedding
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if embedding is None and query_text is not None:
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if self._embedding_generator is not None:
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embedding = await self._embedding_generator.generate(query_text)
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else:
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logger.warning("No embedding generator, falling back to recency")
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recency = RecencyRetriever()
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fallback_result = await recency.retrieve(
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session, project_id, limit, agent_instance_id=agent_instance_id
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)
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latency_ms = (time.perf_counter() - start_time) * 1000
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return RetrievalResult(
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items=fallback_result.items,
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total_count=fallback_result.total_count,
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query=query_text,
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retrieval_type=RetrievalStrategy.SEMANTIC.value,
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latency_ms=latency_ms,
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metadata={
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"fallback": "recency",
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"reason": "no_embedding_generator",
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},
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)
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# For now, use recency if vector search not available
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# TODO: Implement proper pgvector similarity search when integrated
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logger.debug("Vector search not yet implemented, using recency fallback")
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recency = RecencyRetriever()
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result = await recency.retrieve(
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session, project_id, limit, agent_instance_id=agent_instance_id
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)
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latency_ms = (time.perf_counter() - start_time) * 1000
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return RetrievalResult(
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items=result.items,
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total_count=result.total_count,
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query=query_text or "embedding",
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retrieval_type=RetrievalStrategy.SEMANTIC.value,
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latency_ms=latency_ms,
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metadata={"fallback": "recency"},
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)
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class EpisodeRetriever:
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"""
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Unified episode retrieval service.
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Provides a single interface for all retrieval strategies.
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"""
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def __init__(
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self,
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session: AsyncSession,
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embedding_generator: Any | None = None,
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) -> None:
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"""Initialize retriever with database session."""
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self._session = session
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self._retrievers: dict[RetrievalStrategy, BaseRetriever] = {
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RetrievalStrategy.RECENCY: RecencyRetriever(),
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RetrievalStrategy.OUTCOME: OutcomeRetriever(),
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RetrievalStrategy.IMPORTANCE: ImportanceRetriever(),
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RetrievalStrategy.SEMANTIC: SemanticRetriever(embedding_generator),
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}
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async def retrieve(
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self,
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project_id: UUID,
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strategy: RetrievalStrategy = RetrievalStrategy.RECENCY,
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limit: int = 10,
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**kwargs: Any,
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) -> RetrievalResult[Episode]:
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"""
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Retrieve episodes using the specified strategy.
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Args:
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project_id: Project to search within
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strategy: Retrieval strategy to use
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limit: Maximum number of episodes to return
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**kwargs: Strategy-specific parameters
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Returns:
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RetrievalResult containing matching episodes
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"""
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retriever = self._retrievers.get(strategy)
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if retriever is None:
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raise ValueError(f"Unknown retrieval strategy: {strategy}")
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return await retriever.retrieve(self._session, project_id, limit, **kwargs)
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async def get_recent(
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self,
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project_id: UUID,
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limit: int = 10,
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since: datetime | None = None,
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agent_instance_id: UUID | None = None,
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) -> RetrievalResult[Episode]:
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"""Get recent episodes."""
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return await self.retrieve(
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project_id,
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RetrievalStrategy.RECENCY,
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limit,
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since=since,
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agent_instance_id=agent_instance_id,
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)
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async def get_by_outcome(
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self,
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project_id: UUID,
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outcome: Outcome,
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limit: int = 10,
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agent_instance_id: UUID | None = None,
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) -> RetrievalResult[Episode]:
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"""Get episodes by outcome."""
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return await self.retrieve(
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project_id,
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RetrievalStrategy.OUTCOME,
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limit,
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outcome=outcome,
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agent_instance_id=agent_instance_id,
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)
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async def get_by_task_type(
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self,
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project_id: UUID,
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task_type: str,
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limit: int = 10,
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agent_instance_id: UUID | None = None,
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) -> RetrievalResult[Episode]:
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"""Get episodes by task type."""
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retriever = TaskTypeRetriever()
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return await retriever.retrieve(
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self._session,
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project_id,
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limit,
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task_type=task_type,
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agent_instance_id=agent_instance_id,
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)
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async def get_important(
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self,
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project_id: UUID,
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limit: int = 10,
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min_importance: float = 0.7,
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agent_instance_id: UUID | None = None,
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) -> RetrievalResult[Episode]:
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"""Get high-importance episodes."""
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return await self.retrieve(
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project_id,
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RetrievalStrategy.IMPORTANCE,
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limit,
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min_importance=min_importance,
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agent_instance_id=agent_instance_id,
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)
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async def search_similar(
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self,
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project_id: UUID,
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query: str,
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limit: int = 10,
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agent_instance_id: UUID | None = None,
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) -> RetrievalResult[Episode]:
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"""Search for semantically similar episodes."""
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return await self.retrieve(
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project_id,
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RetrievalStrategy.SEMANTIC,
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limit,
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query_text=query,
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agent_instance_id=agent_instance_id,
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)
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