90758facae
Features: - Three-tier memory architecture (short/mid/long-term) - PostgreSQL schema for conversations, events, memories - Qdrant vector database for semantic search - Cohere embeddings (embed-multilingual-v3.0, 1024 dims) - FastAPI Memory Service with full CRUD - External Secrets integration with Vault - Kubernetes deployment manifests Components: - infrastructure/database/agent-memory-schema.sql - infrastructure/kubernetes/apps/qdrant/ - infrastructure/kubernetes/apps/memory-service/ - services/memory-service/ (FastAPI app) Also includes: - External Secrets Operator - Traefik Ingress Controller - Cert-Manager with Let's Encrypt - ArgoCD for GitOps
484 lines
15 KiB
Python
484 lines
15 KiB
Python
"""
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DAARION Memory Service - FastAPI Application
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Трирівнева пам'ять агентів:
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- Short-term: conversation events (робочий буфер)
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- Mid-term: thread summaries (сесійна/тематична)
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- Long-term: memory items (персональна/проектна)
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"""
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from contextlib import asynccontextmanager
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from typing import List, Optional
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from uuid import UUID
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import structlog
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from fastapi import FastAPI, HTTPException, Query
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from fastapi.middleware.cors import CORSMiddleware
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from .config import get_settings
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from .models import (
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CreateThreadRequest, AddEventRequest, CreateMemoryRequest,
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MemoryFeedbackRequest, RetrievalRequest, SummaryRequest,
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ThreadResponse, EventResponse, MemoryResponse,
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SummaryResponse, RetrievalResponse, RetrievalResult,
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ContextResponse, MemoryCategory, FeedbackAction
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)
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from .vector_store import vector_store
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from .database import db
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logger = structlog.get_logger()
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settings = get_settings()
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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"""Startup and shutdown events"""
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# Startup
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logger.info("starting_memory_service")
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await db.connect()
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await vector_store.initialize()
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yield
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# Shutdown
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await db.disconnect()
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logger.info("memory_service_stopped")
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app = FastAPI(
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title="DAARION Memory Service",
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description="Agent memory management with PostgreSQL + Qdrant + Cohere",
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version="1.0.0",
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lifespan=lifespan
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)
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# ============================================================================
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# HEALTH
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# ============================================================================
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@app.get("/health")
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async def health():
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"""Health check"""
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return {
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"status": "healthy",
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"service": settings.service_name,
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"vector_store": await vector_store.get_collection_stats()
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}
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# ============================================================================
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# THREADS (Conversations)
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# ============================================================================
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@app.post("/threads", response_model=ThreadResponse)
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async def create_thread(request: CreateThreadRequest):
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"""Create new conversation thread"""
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thread = await db.create_thread(
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org_id=request.org_id,
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user_id=request.user_id,
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workspace_id=request.workspace_id,
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agent_id=request.agent_id,
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title=request.title,
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tags=request.tags,
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metadata=request.metadata
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)
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return thread
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@app.get("/threads/{thread_id}", response_model=ThreadResponse)
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async def get_thread(thread_id: UUID):
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"""Get thread by ID"""
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thread = await db.get_thread(thread_id)
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if not thread:
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raise HTTPException(status_code=404, detail="Thread not found")
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return thread
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@app.get("/threads", response_model=List[ThreadResponse])
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async def list_threads(
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user_id: UUID = Query(...),
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org_id: UUID = Query(...),
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workspace_id: Optional[UUID] = None,
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agent_id: Optional[UUID] = None,
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limit: int = Query(default=20, le=100)
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):
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"""List threads for user"""
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threads = await db.list_threads(
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org_id=org_id,
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user_id=user_id,
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workspace_id=workspace_id,
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agent_id=agent_id,
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limit=limit
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)
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return threads
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# ============================================================================
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# EVENTS (Short-term Memory)
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# ============================================================================
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@app.post("/events", response_model=EventResponse)
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async def add_event(request: AddEventRequest):
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"""Add event to conversation (message, tool call, etc.)"""
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event = await db.add_event(
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thread_id=request.thread_id,
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event_type=request.event_type,
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role=request.role,
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content=request.content,
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tool_name=request.tool_name,
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tool_input=request.tool_input,
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tool_output=request.tool_output,
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payload=request.payload,
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token_count=request.token_count,
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model_used=request.model_used,
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latency_ms=request.latency_ms,
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metadata=request.metadata
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)
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return event
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@app.get("/threads/{thread_id}/events", response_model=List[EventResponse])
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async def get_events(
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thread_id: UUID,
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limit: int = Query(default=50, le=200),
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offset: int = Query(default=0)
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):
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"""Get events for thread (most recent first)"""
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events = await db.get_events(thread_id, limit=limit, offset=offset)
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return events
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# ============================================================================
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# MEMORIES (Long-term Memory)
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# ============================================================================
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@app.post("/memories", response_model=MemoryResponse)
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async def create_memory(request: CreateMemoryRequest):
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"""Create long-term memory item"""
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# Create in PostgreSQL
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memory = await db.create_memory(
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org_id=request.org_id,
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user_id=request.user_id,
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workspace_id=request.workspace_id,
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agent_id=request.agent_id,
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category=request.category,
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fact_text=request.fact_text,
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confidence=request.confidence,
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source_event_id=request.source_event_id,
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source_thread_id=request.source_thread_id,
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extraction_method=request.extraction_method,
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is_sensitive=request.is_sensitive,
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retention=request.retention,
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ttl_days=request.ttl_days,
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tags=request.tags,
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metadata=request.metadata
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)
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# Index in Qdrant
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point_id = await vector_store.index_memory(
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memory_id=memory["memory_id"],
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text=request.fact_text,
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org_id=request.org_id,
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user_id=request.user_id,
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category=request.category,
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agent_id=request.agent_id,
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workspace_id=request.workspace_id,
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thread_id=request.source_thread_id
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)
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# Update memory with embedding ID
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await db.update_memory_embedding_id(memory["memory_id"], point_id)
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return memory
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@app.get("/memories/{memory_id}", response_model=MemoryResponse)
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async def get_memory(memory_id: UUID):
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"""Get memory by ID"""
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memory = await db.get_memory(memory_id)
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if not memory:
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raise HTTPException(status_code=404, detail="Memory not found")
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return memory
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@app.get("/memories", response_model=List[MemoryResponse])
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async def list_memories(
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user_id: UUID = Query(...),
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org_id: UUID = Query(...),
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agent_id: Optional[UUID] = None,
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workspace_id: Optional[UUID] = None,
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category: Optional[MemoryCategory] = None,
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include_global: bool = True,
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limit: int = Query(default=50, le=200)
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):
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"""List memories for user"""
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memories = await db.list_memories(
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org_id=org_id,
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user_id=user_id,
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agent_id=agent_id,
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workspace_id=workspace_id,
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category=category,
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include_global=include_global,
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limit=limit
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)
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return memories
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@app.post("/memories/{memory_id}/feedback")
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async def memory_feedback(memory_id: UUID, request: MemoryFeedbackRequest):
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"""User feedback on memory (confirm/reject/edit/delete)"""
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memory = await db.get_memory(memory_id)
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if not memory:
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raise HTTPException(status_code=404, detail="Memory not found")
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# Record feedback
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await db.add_memory_feedback(
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memory_id=memory_id,
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user_id=request.user_id,
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action=request.action,
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old_value=memory["fact_text"],
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new_value=request.new_value,
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reason=request.reason
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)
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# Apply action
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if request.action == FeedbackAction.CONFIRM:
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new_confidence = min(1.0, memory["confidence"] + settings.memory_confirm_boost)
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await db.update_memory_confidence(memory_id, new_confidence, verified=True)
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elif request.action == FeedbackAction.REJECT:
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new_confidence = max(0.0, memory["confidence"] - settings.memory_reject_penalty)
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if new_confidence < settings.memory_min_confidence:
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# Mark as invalid
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await db.invalidate_memory(memory_id)
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await vector_store.delete_memory(memory_id)
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else:
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await db.update_memory_confidence(memory_id, new_confidence)
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elif request.action == FeedbackAction.EDIT:
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if request.new_value:
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await db.update_memory_text(memory_id, request.new_value)
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# Re-index with new text
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await vector_store.delete_memory(memory_id)
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await vector_store.index_memory(
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memory_id=memory_id,
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text=request.new_value,
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org_id=memory["org_id"],
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user_id=memory["user_id"],
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category=memory["category"],
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agent_id=memory.get("agent_id"),
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workspace_id=memory.get("workspace_id")
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)
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elif request.action == FeedbackAction.DELETE:
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await db.invalidate_memory(memory_id)
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await vector_store.delete_memory(memory_id)
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return {"status": "ok", "action": request.action.value}
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# ============================================================================
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# RETRIEVAL (Semantic Search)
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# ============================================================================
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@app.post("/retrieve", response_model=RetrievalResponse)
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async def retrieve_memories(request: RetrievalRequest):
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"""
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Semantic retrieval of relevant memories.
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Performs multiple queries and deduplicates results.
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"""
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all_results = []
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seen_ids = set()
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for query in request.queries:
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results = await vector_store.search_memories(
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query=query,
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org_id=request.org_id,
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user_id=request.user_id,
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agent_id=request.agent_id,
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workspace_id=request.workspace_id,
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categories=request.categories,
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include_global=request.include_global,
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top_k=request.top_k
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)
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for r in results:
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memory_id = r.get("memory_id")
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if memory_id and memory_id not in seen_ids:
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seen_ids.add(memory_id)
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# Get full memory from DB for confidence check
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memory = await db.get_memory(UUID(memory_id))
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if memory and memory["confidence"] >= request.min_confidence:
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all_results.append(RetrievalResult(
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memory_id=UUID(memory_id),
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fact_text=r["text"],
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category=MemoryCategory(r["category"]),
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confidence=memory["confidence"],
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relevance_score=r["score"],
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agent_id=UUID(r["agent_id"]) if r.get("agent_id") else None,
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is_global=r.get("agent_id") is None
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))
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# Update usage stats
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await db.increment_memory_usage(UUID(memory_id))
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# Sort by relevance
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all_results.sort(key=lambda x: x.relevance_score, reverse=True)
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return RetrievalResponse(
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results=all_results[:request.top_k],
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query_count=len(request.queries),
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total_results=len(all_results)
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)
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# ============================================================================
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# SUMMARIES (Mid-term Memory)
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# ============================================================================
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@app.post("/threads/{thread_id}/summarize", response_model=SummaryResponse)
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async def create_summary(thread_id: UUID, request: SummaryRequest):
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"""
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Generate rolling summary for thread.
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Compresses old events into a structured summary.
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"""
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thread = await db.get_thread(thread_id)
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if not thread:
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raise HTTPException(status_code=404, detail="Thread not found")
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# Check if summary is needed
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if not request.force and thread["total_tokens"] < settings.summary_trigger_tokens:
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raise HTTPException(
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status_code=400,
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detail=f"Token count ({thread['total_tokens']}) below threshold ({settings.summary_trigger_tokens})"
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)
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# Get events to summarize
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events = await db.get_events_for_summary(thread_id)
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# TODO: Call LLM to generate summary
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# For now, create a placeholder
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summary_text = f"Summary of {len(events)} events. [Implement LLM summarization]"
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state = {
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"goals": [],
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"decisions": [],
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"open_questions": [],
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"next_steps": [],
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"key_facts": []
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}
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# Create summary
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summary = await db.create_summary(
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thread_id=thread_id,
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summary_text=summary_text,
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state=state,
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events_from_seq=events[0]["sequence_num"] if events else 0,
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events_to_seq=events[-1]["sequence_num"] if events else 0,
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events_count=len(events)
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)
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# Index summary in Qdrant
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await vector_store.index_summary(
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summary_id=summary["summary_id"],
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text=summary_text,
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thread_id=thread_id,
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org_id=thread["org_id"],
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user_id=thread["user_id"],
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agent_id=thread.get("agent_id"),
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workspace_id=thread.get("workspace_id")
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)
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return summary
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@app.get("/threads/{thread_id}/summary", response_model=Optional[SummaryResponse])
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async def get_latest_summary(thread_id: UUID):
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"""Get latest summary for thread"""
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summary = await db.get_latest_summary(thread_id)
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return summary
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# ============================================================================
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# CONTEXT (Full context for agent)
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# ============================================================================
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@app.get("/threads/{thread_id}/context", response_model=ContextResponse)
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async def get_context(
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thread_id: UUID,
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queries: List[str] = Query(default=[]),
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top_k: int = Query(default=10)
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):
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"""
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Get full context for agent prompt.
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Combines:
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- Latest summary (mid-term)
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- Recent messages (short-term)
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- Retrieved memories (long-term)
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"""
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thread = await db.get_thread(thread_id)
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if not thread:
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raise HTTPException(status_code=404, detail="Thread not found")
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# Get summary
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summary = await db.get_latest_summary(thread_id)
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# Get recent messages
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recent = await db.get_events(
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thread_id,
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limit=settings.short_term_window_messages
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)
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# Retrieve memories if queries provided
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retrieved = []
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if queries:
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retrieval_response = await retrieve_memories(RetrievalRequest(
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org_id=thread["org_id"],
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user_id=thread["user_id"],
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agent_id=thread.get("agent_id"),
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workspace_id=thread.get("workspace_id"),
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queries=queries,
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top_k=top_k,
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include_global=True
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))
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retrieved = retrieval_response.results
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# Estimate tokens
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token_estimate = sum(e.get("token_count", 0) or 0 for e in recent)
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if summary:
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token_estimate += summary.get("summary_tokens", 0) or 0
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return ContextResponse(
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thread_id=thread_id,
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summary=summary,
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recent_messages=recent,
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retrieved_memories=retrieved,
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token_estimate=token_estimate
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)
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# ============================================================================
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# ADMIN
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# ============================================================================
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@app.get("/stats")
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async def get_stats():
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"""Get service statistics"""
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return {
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"vector_store": await vector_store.get_collection_stats(),
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"database": await db.get_stats()
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}
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8000)
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