abfcc91eb3
- Fix bare except clauses in curator.py and main.py - Change embedding model to snowflake-arctic-embed2 - Increase semantic_score_threshold to 0.6 - Add memory context explanation to systemprompt.md - Add pytest dependencies to requirements.txt - Remove unused context_handler.py and .env.example - Add project documentation (CLAUDE.md) and test files Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
136 lines
4.1 KiB
Python
136 lines
4.1 KiB
Python
# app/main.py
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from fastapi import FastAPI, Request
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from fastapi.responses import StreamingResponse, JSONResponse
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from contextlib import asynccontextmanager
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import httpx
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import logging
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from datetime import datetime
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from .config import config
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from .singleton import get_qdrant_service
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from .proxy_handler import handle_chat, forward_to_ollama, handle_chat_non_streaming
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from .curator import Curator
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from apscheduler.schedulers.asyncio import AsyncIOScheduler
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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scheduler = AsyncIOScheduler()
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curator = None
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async def run_curator():
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"""Scheduled daily curator job.
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Runs every day at configured time. The curator itself detects
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if it's day 01 (monthly mode) and processes all memories.
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Otherwise processes recent 24h only.
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"""
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global curator
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logger.info("Starting memory curation...")
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try:
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await curator.run()
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logger.info("Memory curation completed successfully")
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except Exception as e:
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logger.error(f"Memory curation failed: {e}")
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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"""Application lifespan - startup and shutdown."""
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global curator
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logger.info("Starting Vera-AI...")
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# Initialize singleton QdrantService
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qdrant_service = get_qdrant_service()
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await qdrant_service._ensure_collection()
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# Initialize curator with singleton
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curator = Curator(
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qdrant_service=qdrant_service,
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model=config.curator_model,
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ollama_host=config.ollama_host
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)
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# Schedule daily curator
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# Note: Monthly mode is detected automatically by curator_prompt.md (day 01)
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hour, minute = map(int, config.run_time.split(":"))
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scheduler.add_job(run_curator, "cron", hour=hour, minute=minute, id="daily_curator")
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logger.info(f"Daily curator scheduled at {config.run_time}")
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scheduler.start()
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yield
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logger.info("Shutting down Vera-AI...")
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scheduler.shutdown()
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await qdrant_service.close()
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app = FastAPI(title="Vera-AI", version="2.0.0", lifespan=lifespan)
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@app.get("/")
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async def health_check():
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"""Health check endpoint."""
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ollama_status = "unreachable"
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try:
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async with httpx.AsyncClient(timeout=5.0) as client:
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resp = await client.get(f"{config.ollama_host}/api/tags")
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if resp.status_code == 200:
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ollama_status = "reachable"
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except Exception:
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logger.warning(f"Failed to reach Ollama at {config.ollama_host}")
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return {"status": "ok", "ollama": ollama_status}
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@app.get("/api/tags")
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async def api_tags():
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"""Proxy to Ollama /api/tags with cloud model injection."""
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async with httpx.AsyncClient() as client:
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resp = await client.get(f"{config.ollama_host}/api/tags")
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data = resp.json()
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if config.cloud.enabled and config.cloud.models:
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for name in config.cloud.models.keys():
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data["models"].append({
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"name": name,
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"modified_at": "2026-03-25T00:00:00Z",
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"size": 0,
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"digest": "cloud",
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"details": {"family": "cloud"}
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})
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return JSONResponse(content=data)
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@app.api_route("/api/{path:path}", methods=["GET", "POST", "DELETE"])
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async def proxy_all(request: Request, path: str):
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if path == "chat":
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body = await request.json()
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is_stream = body.get("stream", True)
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if is_stream:
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return await handle_chat(request)
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else:
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return await handle_chat_non_streaming(body)
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else:
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resp = await forward_to_ollama(request, f"/api/{path}")
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return StreamingResponse(
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resp.aiter_bytes(),
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status_code=resp.status_code,
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headers=dict(resp.headers),
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media_type=resp.headers.get("content-type")
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)
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@app.post("/curator/run")
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async def trigger_curator():
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"""Manually trigger curator.
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The curator will automatically detect if it's day 01 (monthly mode)
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and process all memories. Otherwise processes recent 24h.
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"""
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await run_curator()
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return {"status": "curation completed"}
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