3b924118be
Brand commands (~290 lines): - Code was trapped inside `if reply_to_message:` block (unreachable) - Moved to feature flag: ENABLE_BRAND_COMMANDS=true to activate - Zero re-indentation: 8sp code naturally fits as feature flag body - Helper functions (_brand_*, _artifact_*) unchanged Memory LLM Summary: - Replace placeholder with real DeepSeek API integration - Structured output: summary, goals, decisions, open_questions, next_steps, key_facts - Graceful fallback if API key not set or call fails - Added MEMORY_DEEPSEEK_API_KEY config - Ukrainian output language Deployed and verified on NODE1. Co-authored-by: Cursor <cursoragent@cursor.com>
1014 lines
34 KiB
Python
1014 lines
34 KiB
Python
"""
|
|
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, Dict, Any
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from fastapi import Depends, BackgroundTasks
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from uuid import UUID
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import structlog
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import httpx
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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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from .auth import get_current_service, get_current_service_optional
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logger = structlog.get_logger()
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settings = get_settings()
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|
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SUMMARY_SYSTEM_PROMPT = """You are a conversation summarizer for DAARION AI agents.
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Given a list of conversation events, produce a structured summary in Ukrainian.
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Output format (JSON):
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{
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"summary": "Стислий опис розмови (2-5 речень)",
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"goals": ["Ціль 1", "Ціль 2"],
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"decisions": ["Рішення 1", "Рішення 2"],
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"open_questions": ["Питання 1"],
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"next_steps": ["Крок 1", "Крок 2"],
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"key_facts": ["Факт 1", "Факт 2"]
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}
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Rules:
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- Write in Ukrainian
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- Be concise but preserve key information
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- Focus on actionable items and decisions
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- If no items for a category, use empty array []
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- Return ONLY valid JSON, no markdown
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"""
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async def _llm_generate_summary(events: List[Dict[str, Any]]) -> Dict[str, Any]:
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"""
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Call DeepSeek API to generate structured thread summary.
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Falls back to placeholder if API key is not configured or call fails.
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"""
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if not settings.deepseek_api_key:
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logger.warning("deepseek_api_key_not_set", hint="Set MEMORY_DEEPSEEK_API_KEY to enable LLM summaries")
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return {
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"summary": f"Summary of {len(events)} events. [LLM not configured — set MEMORY_DEEPSEEK_API_KEY]",
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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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# Format events for LLM
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events_text = []
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for ev in events[-50:]: # Limit to last 50 events to stay within context
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role = ev.get("role", "unknown")
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content = ev.get("content", "")[:500] # Truncate long messages
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events_text.append(f"[{role}]: {content}")
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conversation = "\n".join(events_text)
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try:
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async with httpx.AsyncClient(timeout=30.0) as client:
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resp = await client.post(
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settings.deepseek_api_url,
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headers={
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"Authorization": f"Bearer {settings.deepseek_api_key}",
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"Content-Type": "application/json",
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},
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json={
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"model": settings.deepseek_model,
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"messages": [
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{"role": "system", "content": SUMMARY_SYSTEM_PROMPT},
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{"role": "user", "content": f"Summarize this conversation:\n\n{conversation}"},
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],
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"max_tokens": settings.summary_max_tokens,
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"temperature": 0.3,
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},
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)
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resp.raise_for_status()
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data = resp.json()
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content = data["choices"][0]["message"]["content"]
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# Parse JSON response
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import json
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try:
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parsed = json.loads(content)
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return {
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"summary": parsed.get("summary", content),
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"goals": parsed.get("goals", []),
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"decisions": parsed.get("decisions", []),
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"open_questions": parsed.get("open_questions", []),
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"next_steps": parsed.get("next_steps", []),
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"key_facts": parsed.get("key_facts", []),
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}
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except json.JSONDecodeError:
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# LLM returned non-JSON — use raw text as summary
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logger.warning("llm_summary_not_json", content_preview=content[:100])
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return {
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"summary": content,
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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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except Exception as e:
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logger.error("llm_summary_failed", error=str(e))
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return {
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"summary": f"Summary of {len(events)} events. [LLM call failed: {str(e)[:100]}]",
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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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|
|
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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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|
|
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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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# ============================================================================
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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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# ============================================================================
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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(
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request: CreateThreadRequest,
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service: Optional[dict] = Depends(get_current_service_optional)
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):
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"""Create new conversation thread"""
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# Auth опціональний: якщо JWT надано, перевіряємо; якщо ні - дозволяємо (dev режим)
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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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|
|
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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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|
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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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|
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@app.post("/events", response_model=EventResponse)
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async def add_event(
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request: AddEventRequest,
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service: Optional[dict] = Depends(get_current_service_optional)
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):
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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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|
):
|
|
"""Get events for thread (most recent first)"""
|
|
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(
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|
request: CreateMemoryRequest,
|
|
service: Optional[dict] = Depends(get_current_service_optional)
|
|
):
|
|
"""Create long-term memory item"""
|
|
# 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,
|
|
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,
|
|
retention=request.retention,
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|
ttl_days=request.ttl_days,
|
|
tags=request.tags,
|
|
metadata=request.metadata
|
|
)
|
|
|
|
# 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,
|
|
user_id=request.user_id,
|
|
category=request.category,
|
|
agent_id=request.agent_id,
|
|
workspace_id=request.workspace_id,
|
|
thread_id=request.source_thread_id
|
|
)
|
|
|
|
# Update memory with embedding ID
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|
await db.update_memory_embedding_id(memory["memory_id"], point_id)
|
|
|
|
return memory
|
|
|
|
|
|
@app.get("/memories/{memory_id}", response_model=MemoryResponse)
|
|
async def get_memory(memory_id: UUID):
|
|
"""Get memory by ID"""
|
|
memory = await db.get_memory(memory_id)
|
|
if not memory:
|
|
raise HTTPException(status_code=404, detail="Memory not found")
|
|
return memory
|
|
|
|
|
|
@app.get("/memories", response_model=List[MemoryResponse])
|
|
async def list_memories(
|
|
user_id: UUID = Query(...),
|
|
org_id: UUID = Query(...),
|
|
agent_id: Optional[UUID] = None,
|
|
workspace_id: Optional[UUID] = None,
|
|
category: Optional[MemoryCategory] = None,
|
|
include_global: bool = True,
|
|
limit: int = Query(default=50, le=200)
|
|
):
|
|
"""List memories for user"""
|
|
memories = await db.list_memories(
|
|
org_id=org_id,
|
|
user_id=user_id,
|
|
agent_id=agent_id,
|
|
workspace_id=workspace_id,
|
|
category=category,
|
|
include_global=include_global,
|
|
limit=limit
|
|
)
|
|
return memories
|
|
|
|
|
|
@app.post("/memories/{memory_id}/feedback")
|
|
async def memory_feedback(memory_id: UUID, request: MemoryFeedbackRequest):
|
|
"""User feedback on memory (confirm/reject/edit/delete)"""
|
|
memory = await db.get_memory(memory_id)
|
|
if not memory:
|
|
raise HTTPException(status_code=404, detail="Memory not found")
|
|
|
|
# Record feedback
|
|
await db.add_memory_feedback(
|
|
memory_id=memory_id,
|
|
user_id=request.user_id,
|
|
action=request.action,
|
|
old_value=memory["fact_text"],
|
|
new_value=request.new_value,
|
|
reason=request.reason
|
|
)
|
|
|
|
# Apply action
|
|
if request.action == FeedbackAction.CONFIRM:
|
|
new_confidence = min(1.0, memory["confidence"] + settings.memory_confirm_boost)
|
|
await db.update_memory_confidence(memory_id, new_confidence, verified=True)
|
|
|
|
elif request.action == FeedbackAction.REJECT:
|
|
new_confidence = max(0.0, memory["confidence"] - settings.memory_reject_penalty)
|
|
if new_confidence < settings.memory_min_confidence:
|
|
# Mark as invalid
|
|
await db.invalidate_memory(memory_id)
|
|
await vector_store.delete_memory(memory_id)
|
|
else:
|
|
await db.update_memory_confidence(memory_id, new_confidence)
|
|
|
|
elif request.action == FeedbackAction.EDIT:
|
|
if request.new_value:
|
|
await db.update_memory_text(memory_id, request.new_value)
|
|
# Re-index with new text
|
|
await vector_store.delete_memory(memory_id)
|
|
await vector_store.index_memory(
|
|
memory_id=memory_id,
|
|
text=request.new_value,
|
|
org_id=memory["org_id"],
|
|
user_id=memory["user_id"],
|
|
category=memory["category"],
|
|
agent_id=memory.get("agent_id"),
|
|
workspace_id=memory.get("workspace_id")
|
|
)
|
|
|
|
elif request.action == FeedbackAction.DELETE:
|
|
await db.invalidate_memory(memory_id)
|
|
await vector_store.delete_memory(memory_id)
|
|
|
|
return {"status": "ok", "action": request.action.value}
|
|
|
|
|
|
# ============================================================================
|
|
# RETRIEVAL (Semantic Search)
|
|
# ============================================================================
|
|
|
|
@app.post("/retrieve", response_model=RetrievalResponse)
|
|
async def retrieve_memories(request: RetrievalRequest):
|
|
"""
|
|
Semantic retrieval of relevant memories.
|
|
|
|
Performs multiple queries and deduplicates results.
|
|
"""
|
|
all_results = []
|
|
seen_ids = set()
|
|
|
|
for query in request.queries:
|
|
results = await vector_store.search_memories(
|
|
query=query,
|
|
org_id=request.org_id,
|
|
user_id=request.user_id,
|
|
agent_id=request.agent_id,
|
|
workspace_id=request.workspace_id,
|
|
categories=request.categories,
|
|
include_global=request.include_global,
|
|
top_k=request.top_k
|
|
)
|
|
|
|
for r in results:
|
|
memory_id = r.get("memory_id")
|
|
if memory_id and memory_id not in seen_ids:
|
|
seen_ids.add(memory_id)
|
|
|
|
# Get full memory from DB for confidence check
|
|
memory = await db.get_memory(UUID(memory_id))
|
|
if memory and memory["confidence"] >= request.min_confidence:
|
|
all_results.append(RetrievalResult(
|
|
memory_id=UUID(memory_id),
|
|
fact_text=r["text"],
|
|
category=MemoryCategory(r["category"]),
|
|
confidence=memory["confidence"],
|
|
relevance_score=r["score"],
|
|
agent_id=UUID(r["agent_id"]) if r.get("agent_id") else None,
|
|
is_global=r.get("agent_id") is None
|
|
))
|
|
|
|
# Update usage stats
|
|
await db.increment_memory_usage(UUID(memory_id))
|
|
|
|
# Sort by relevance
|
|
all_results.sort(key=lambda x: x.relevance_score, reverse=True)
|
|
|
|
return RetrievalResponse(
|
|
results=all_results[:request.top_k],
|
|
query_count=len(request.queries),
|
|
total_results=len(all_results)
|
|
)
|
|
|
|
|
|
# ============================================================================
|
|
# SUMMARIES (Mid-term Memory)
|
|
# ============================================================================
|
|
|
|
@app.post("/threads/{thread_id}/summarize", response_model=SummaryResponse)
|
|
async def create_summary(thread_id: UUID, request: SummaryRequest):
|
|
"""
|
|
Generate rolling summary for thread.
|
|
|
|
Compresses old events into a structured summary.
|
|
"""
|
|
thread = await db.get_thread(thread_id)
|
|
if not thread:
|
|
raise HTTPException(status_code=404, detail="Thread not found")
|
|
|
|
# Check if summary is needed
|
|
if not request.force and thread["total_tokens"] < settings.summary_trigger_tokens:
|
|
raise HTTPException(
|
|
status_code=400,
|
|
detail=f"Token count ({thread['total_tokens']}) below threshold ({settings.summary_trigger_tokens})"
|
|
)
|
|
|
|
# Get events to summarize
|
|
events = await db.get_events_for_summary(thread_id)
|
|
|
|
# Generate structured summary via DeepSeek LLM
|
|
llm_result = await _llm_generate_summary(events)
|
|
summary_text = llm_result["summary"]
|
|
state = {
|
|
"goals": llm_result["goals"],
|
|
"decisions": llm_result["decisions"],
|
|
"open_questions": llm_result["open_questions"],
|
|
"next_steps": llm_result["next_steps"],
|
|
"key_facts": llm_result["key_facts"]
|
|
}
|
|
|
|
# Create summary
|
|
summary = await db.create_summary(
|
|
thread_id=thread_id,
|
|
summary_text=summary_text,
|
|
state=state,
|
|
events_from_seq=events[0]["sequence_num"] if events else 0,
|
|
events_to_seq=events[-1]["sequence_num"] if events else 0,
|
|
events_count=len(events)
|
|
)
|
|
|
|
# Index summary in Qdrant
|
|
await vector_store.index_summary(
|
|
summary_id=summary["summary_id"],
|
|
text=summary_text,
|
|
thread_id=thread_id,
|
|
org_id=thread["org_id"],
|
|
user_id=thread["user_id"],
|
|
agent_id=thread.get("agent_id"),
|
|
workspace_id=thread.get("workspace_id")
|
|
)
|
|
|
|
return summary
|
|
|
|
|
|
@app.get("/threads/{thread_id}/summary", response_model=Optional[SummaryResponse])
|
|
async def get_latest_summary(thread_id: UUID):
|
|
"""Get latest summary for thread"""
|
|
summary = await db.get_latest_summary(thread_id)
|
|
return summary
|
|
|
|
|
|
# ============================================================================
|
|
# CONTEXT (Full context for agent)
|
|
# ============================================================================
|
|
|
|
@app.get("/threads/{thread_id}/context", response_model=ContextResponse)
|
|
async def get_context(
|
|
thread_id: UUID,
|
|
queries: List[str] = Query(default=[]),
|
|
top_k: int = Query(default=10)
|
|
):
|
|
"""
|
|
Get full context for agent prompt.
|
|
|
|
Combines:
|
|
- Latest summary (mid-term)
|
|
- Recent messages (short-term)
|
|
- Retrieved memories (long-term)
|
|
"""
|
|
thread = await db.get_thread(thread_id)
|
|
if not thread:
|
|
raise HTTPException(status_code=404, detail="Thread not found")
|
|
|
|
# Get summary
|
|
summary = await db.get_latest_summary(thread_id)
|
|
|
|
# Get recent messages
|
|
recent = await db.get_events(
|
|
thread_id,
|
|
limit=settings.short_term_window_messages
|
|
)
|
|
|
|
# Retrieve memories if queries provided
|
|
retrieved = []
|
|
if queries:
|
|
retrieval_response = await retrieve_memories(RetrievalRequest(
|
|
org_id=thread["org_id"],
|
|
user_id=thread["user_id"],
|
|
agent_id=thread.get("agent_id"),
|
|
workspace_id=thread.get("workspace_id"),
|
|
queries=queries,
|
|
top_k=top_k,
|
|
include_global=True
|
|
))
|
|
retrieved = retrieval_response.results
|
|
|
|
# Estimate tokens
|
|
token_estimate = sum(e.get("token_count", 0) or 0 for e in recent)
|
|
if summary:
|
|
token_estimate += summary.get("summary_tokens", 0) or 0
|
|
|
|
return ContextResponse(
|
|
thread_id=thread_id,
|
|
summary=summary,
|
|
recent_messages=recent,
|
|
retrieved_memories=retrieved,
|
|
token_estimate=token_estimate
|
|
)
|
|
|
|
|
|
# ============================================================================
|
|
# FACTS (Simple Key-Value storage for Gateway compatibility)
|
|
# ============================================================================
|
|
|
|
from pydantic import BaseModel
|
|
from typing import Any
|
|
|
|
class FactUpsertRequest(BaseModel):
|
|
"""Request to upsert a user fact"""
|
|
user_id: str
|
|
fact_key: str
|
|
fact_value: Optional[str] = None
|
|
fact_value_json: Optional[dict] = None
|
|
team_id: Optional[str] = None
|
|
|
|
@app.post("/facts/upsert")
|
|
async def upsert_fact(request: FactUpsertRequest):
|
|
"""
|
|
Create or update a user fact.
|
|
|
|
This is a simple key-value store for Gateway compatibility.
|
|
Facts are stored in PostgreSQL without vector indexing.
|
|
"""
|
|
try:
|
|
# Ensure facts table exists (will be created on first call)
|
|
await db.ensure_facts_table()
|
|
|
|
# Upsert the fact
|
|
result = await db.upsert_fact(
|
|
user_id=request.user_id,
|
|
fact_key=request.fact_key,
|
|
fact_value=request.fact_value,
|
|
fact_value_json=request.fact_value_json,
|
|
team_id=request.team_id
|
|
)
|
|
|
|
logger.info(f"fact_upserted", user_id=request.user_id, fact_key=request.fact_key)
|
|
return {"status": "ok", "fact_id": result.get("fact_id") if result else None}
|
|
|
|
except Exception as e:
|
|
logger.error(f"fact_upsert_failed", error=str(e), user_id=request.user_id)
|
|
raise HTTPException(status_code=500, detail=str(e))
|
|
|
|
|
|
@app.get("/facts/{fact_key}")
|
|
async def get_fact(
|
|
fact_key: str,
|
|
user_id: str = Query(...),
|
|
team_id: Optional[str] = None
|
|
):
|
|
"""Get a specific fact for a user"""
|
|
try:
|
|
fact = await db.get_fact(user_id=user_id, fact_key=fact_key, team_id=team_id)
|
|
if not fact:
|
|
raise HTTPException(status_code=404, detail="Fact not found")
|
|
return fact
|
|
except HTTPException:
|
|
raise
|
|
except Exception as e:
|
|
logger.error(f"fact_get_failed", error=str(e))
|
|
raise HTTPException(status_code=500, detail=str(e))
|
|
|
|
|
|
@app.get("/facts")
|
|
async def list_facts(
|
|
user_id: str = Query(...),
|
|
team_id: Optional[str] = None
|
|
):
|
|
"""List all facts for a user"""
|
|
try:
|
|
facts = await db.list_facts(user_id=user_id, team_id=team_id)
|
|
return {"facts": facts}
|
|
except Exception as e:
|
|
logger.error(f"facts_list_failed", error=str(e))
|
|
raise HTTPException(status_code=500, detail=str(e))
|
|
|
|
|
|
@app.delete("/facts/{fact_key}")
|
|
async def delete_fact(
|
|
fact_key: str,
|
|
user_id: str = Query(...),
|
|
team_id: Optional[str] = None
|
|
):
|
|
"""Delete a fact"""
|
|
try:
|
|
deleted = await db.delete_fact(user_id=user_id, fact_key=fact_key, team_id=team_id)
|
|
if not deleted:
|
|
raise HTTPException(status_code=404, detail="Fact not found")
|
|
return {"status": "ok", "deleted": True}
|
|
except HTTPException:
|
|
raise
|
|
except Exception as e:
|
|
logger.error(f"fact_delete_failed", error=str(e))
|
|
raise HTTPException(status_code=500, detail=str(e))
|
|
|
|
|
|
# ============================================================================
|
|
# AGENT MEMORY (Gateway compatibility endpoint)
|
|
# ============================================================================
|
|
|
|
class AgentMemoryRequest(BaseModel):
|
|
"""Request format from Gateway for saving chat history"""
|
|
agent_id: str
|
|
team_id: Optional[str] = None
|
|
channel_id: Optional[str] = None
|
|
user_id: str
|
|
# Support both formats: new (content) and gateway (body_text)
|
|
content: Optional[str] = None
|
|
body_text: Optional[str] = None
|
|
role: str = "user" # user, assistant, system
|
|
# Support both formats: metadata and body_json
|
|
metadata: Optional[dict] = None
|
|
body_json: Optional[dict] = None
|
|
context: Optional[str] = None
|
|
scope: Optional[str] = None
|
|
kind: Optional[str] = None # "message", "event", etc.
|
|
|
|
def get_content(self) -> str:
|
|
"""Get content from either field"""
|
|
return self.content or self.body_text or ""
|
|
|
|
def get_metadata(self) -> dict:
|
|
"""Get metadata from either field"""
|
|
return self.metadata or self.body_json or {}
|
|
|
|
@app.post("/agents/{agent_id}/memory")
|
|
async def save_agent_memory(agent_id: str, request: AgentMemoryRequest, background_tasks: BackgroundTasks):
|
|
"""
|
|
Save chat turn to memory with full ingestion pipeline:
|
|
1. Save to PostgreSQL (facts table)
|
|
2. Create embedding via Cohere and save to Qdrant
|
|
3. Update Knowledge Graph in Neo4j
|
|
"""
|
|
try:
|
|
from datetime import datetime
|
|
from uuid import uuid4
|
|
|
|
# Create a unique key for this conversation event
|
|
timestamp = datetime.utcnow().isoformat()
|
|
message_id = str(uuid4())
|
|
fact_key = f"chat_event:{request.channel_id}:{timestamp}"
|
|
|
|
# Store as a fact with JSON payload
|
|
content = request.get_content()
|
|
metadata = request.get_metadata()
|
|
|
|
# Skip empty messages
|
|
if not content or content.startswith("[Photo:"):
|
|
logger.debug("skipping_empty_or_photo_message", content=content[:50] if content else "")
|
|
return {"status": "ok", "event_id": None, "indexed": False}
|
|
|
|
# Determine role from kind/body_json if not explicitly set
|
|
role = request.role
|
|
if request.body_json and request.body_json.get("type") == "agent_response":
|
|
role = "assistant"
|
|
|
|
event_data = {
|
|
"message_id": message_id,
|
|
"agent_id": agent_id,
|
|
"team_id": request.team_id,
|
|
"channel_id": request.channel_id,
|
|
"user_id": request.user_id,
|
|
"role": role,
|
|
"content": content,
|
|
"metadata": metadata,
|
|
"scope": request.scope,
|
|
"kind": request.kind,
|
|
"timestamp": timestamp
|
|
}
|
|
|
|
# 1. Save to PostgreSQL (isolated by agent_id)
|
|
await db.ensure_facts_table()
|
|
result = await db.upsert_fact(
|
|
user_id=request.user_id,
|
|
fact_key=fact_key,
|
|
fact_value_json=event_data,
|
|
team_id=request.team_id,
|
|
agent_id=agent_id # Agent isolation
|
|
)
|
|
|
|
logger.info("agent_memory_saved",
|
|
agent_id=agent_id,
|
|
user_id=request.user_id,
|
|
role=role,
|
|
channel_id=request.channel_id,
|
|
content_len=len(content))
|
|
|
|
# 2. Index in Qdrant (async background task)
|
|
background_tasks.add_task(
|
|
index_message_in_qdrant,
|
|
message_id=message_id,
|
|
content=content,
|
|
agent_id=agent_id,
|
|
user_id=request.user_id,
|
|
channel_id=request.channel_id,
|
|
role=role,
|
|
timestamp=timestamp
|
|
)
|
|
|
|
# 3. Update Neo4j graph (async background task)
|
|
background_tasks.add_task(
|
|
update_neo4j_graph,
|
|
message_id=message_id,
|
|
content=content,
|
|
agent_id=agent_id,
|
|
user_id=request.user_id,
|
|
channel_id=request.channel_id,
|
|
role=role
|
|
)
|
|
|
|
return {
|
|
"status": "ok",
|
|
"event_id": result.get("fact_id") if result else None,
|
|
"message_id": message_id,
|
|
"indexed": True
|
|
}
|
|
|
|
except Exception as e:
|
|
logger.error("agent_memory_save_failed", error=str(e), agent_id=agent_id)
|
|
raise HTTPException(status_code=500, detail=str(e))
|
|
|
|
|
|
async def index_message_in_qdrant(
|
|
message_id: str,
|
|
content: str,
|
|
agent_id: str,
|
|
user_id: str,
|
|
channel_id: str,
|
|
role: str,
|
|
timestamp: str
|
|
):
|
|
"""Index message in Qdrant for semantic search (isolated by agent_id)"""
|
|
try:
|
|
from .embedding import get_document_embeddings
|
|
from qdrant_client.http import models as qmodels
|
|
|
|
# Skip very short messages
|
|
if len(content) < 10:
|
|
return
|
|
|
|
# Generate embedding
|
|
embeddings = await get_document_embeddings([content])
|
|
if not embeddings or not embeddings[0]:
|
|
logger.warning("embedding_failed", message_id=message_id)
|
|
return
|
|
|
|
vector = embeddings[0]
|
|
|
|
# Use agent-specific collection (isolation!)
|
|
collection_name = f"{agent_id}_messages"
|
|
|
|
# Ensure collection exists
|
|
try:
|
|
vector_store.client.get_collection(collection_name)
|
|
except Exception:
|
|
# Create collection if not exists
|
|
vector_store.client.create_collection(
|
|
collection_name=collection_name,
|
|
vectors_config=qmodels.VectorParams(
|
|
size=len(vector),
|
|
distance=qmodels.Distance.COSINE
|
|
)
|
|
)
|
|
logger.info("created_collection", collection=collection_name)
|
|
|
|
# Save to agent-specific Qdrant collection
|
|
vector_store.client.upsert(
|
|
collection_name=collection_name,
|
|
points=[
|
|
qmodels.PointStruct(
|
|
id=message_id,
|
|
vector=vector,
|
|
payload={
|
|
"message_id": message_id,
|
|
"agent_id": agent_id,
|
|
"user_id": user_id,
|
|
"channel_id": channel_id,
|
|
"role": role,
|
|
"content": content,
|
|
"timestamp": timestamp,
|
|
"type": "chat_message"
|
|
}
|
|
)
|
|
]
|
|
)
|
|
|
|
logger.info("message_indexed_qdrant",
|
|
message_id=message_id,
|
|
collection=collection_name,
|
|
content_len=len(content),
|
|
vector_dim=len(vector))
|
|
|
|
except Exception as e:
|
|
logger.error("qdrant_indexing_failed", error=str(e), message_id=message_id)
|
|
|
|
|
|
async def update_neo4j_graph(
|
|
message_id: str,
|
|
content: str,
|
|
agent_id: str,
|
|
user_id: str,
|
|
channel_id: str,
|
|
role: str
|
|
):
|
|
"""Update Knowledge Graph in Neo4j (with agent isolation)"""
|
|
try:
|
|
import httpx
|
|
import os
|
|
|
|
neo4j_url = os.getenv("NEO4J_HTTP_URL", "http://neo4j:7474")
|
|
neo4j_user = os.getenv("NEO4J_USER", "neo4j")
|
|
neo4j_password = os.getenv("NEO4J_PASSWORD", "DaarionNeo4j2026!")
|
|
|
|
# Create/update User node and Message relationship
|
|
# IMPORTANT: agent_id is added to relationships for filtering
|
|
cypher = """
|
|
MERGE (u:User {user_id: $user_id})
|
|
ON CREATE SET u.created_at = datetime()
|
|
ON MATCH SET u.last_seen = datetime()
|
|
|
|
MERGE (ch:Channel {channel_id: $channel_id})
|
|
ON CREATE SET ch.created_at = datetime()
|
|
|
|
MERGE (a:Agent {agent_id: $agent_id})
|
|
ON CREATE SET a.created_at = datetime()
|
|
|
|
MERGE (u)-[p:PARTICIPATES_IN {agent_id: $agent_id}]->(ch)
|
|
ON CREATE SET p.first_seen = datetime()
|
|
ON MATCH SET p.last_seen = datetime()
|
|
|
|
CREATE (m:Message {
|
|
message_id: $message_id,
|
|
role: $role,
|
|
content_preview: $content_preview,
|
|
agent_id: $agent_id,
|
|
created_at: datetime()
|
|
})
|
|
|
|
CREATE (u)-[:SENT {agent_id: $agent_id}]->(m)
|
|
CREATE (m)-[:IN_CHANNEL {agent_id: $agent_id}]->(ch)
|
|
CREATE (m)-[:HANDLED_BY]->(a)
|
|
|
|
RETURN m.message_id as id
|
|
"""
|
|
|
|
async with httpx.AsyncClient(timeout=10.0) as client:
|
|
response = await client.post(
|
|
f"{neo4j_url}/db/neo4j/tx/commit",
|
|
auth=(neo4j_user, neo4j_password),
|
|
json={
|
|
"statements": [{
|
|
"statement": cypher,
|
|
"parameters": {
|
|
"user_id": user_id,
|
|
"channel_id": channel_id,
|
|
"message_id": message_id,
|
|
"role": role,
|
|
"content_preview": content[:200] if content else "",
|
|
"agent_id": agent_id
|
|
}
|
|
}]
|
|
}
|
|
)
|
|
|
|
if response.status_code == 200:
|
|
logger.info("neo4j_graph_updated", message_id=message_id, user_id=user_id, agent_id=agent_id)
|
|
else:
|
|
logger.warning("neo4j_update_failed",
|
|
status=response.status_code,
|
|
response=response.text[:200])
|
|
|
|
except Exception as e:
|
|
logger.error("neo4j_update_error", error=str(e), message_id=message_id)
|
|
|
|
|
|
@app.get("/agents/{agent_id}/memory")
|
|
async def get_agent_memory(
|
|
agent_id: str,
|
|
user_id: str = Query(...),
|
|
channel_id: Optional[str] = None,
|
|
limit: int = Query(default=20, le=100)
|
|
):
|
|
"""
|
|
Get recent chat events for an agent/user (isolated by agent_id).
|
|
"""
|
|
import json as json_lib
|
|
try:
|
|
# Query facts filtered by agent_id (database-level filtering)
|
|
facts = await db.list_facts(user_id=user_id, agent_id=agent_id, limit=limit)
|
|
|
|
# Filter for chat events from this channel
|
|
events = []
|
|
for fact in facts:
|
|
if fact.get("fact_key", "").startswith("chat_event:"):
|
|
# Handle fact_value_json being string or dict
|
|
event_data = fact.get("fact_value_json", {})
|
|
if isinstance(event_data, str):
|
|
try:
|
|
event_data = json_lib.loads(event_data)
|
|
except:
|
|
event_data = {}
|
|
if not isinstance(event_data, dict):
|
|
event_data = {}
|
|
if channel_id is None or event_data.get("channel_id") == channel_id:
|
|
events.append(event_data)
|
|
|
|
return {"events": events[:limit]}
|
|
|
|
except Exception as e:
|
|
logger.error("agent_memory_get_failed", error=str(e), agent_id=agent_id)
|
|
raise HTTPException(status_code=500, detail=str(e))
|
|
|
|
|
|
# ============================================================================
|
|
# ADMIN
|
|
# ============================================================================
|
|
|
|
@app.get("/stats")
|
|
async def get_stats():
|
|
"""Get service statistics"""
|
|
return {
|
|
"vector_store": await vector_store.get_collection_stats(),
|
|
"database": await db.get_stats()
|
|
}
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import uvicorn
|
|
uvicorn.run(app, host="0.0.0.0", port=8000)
|