Initial commit: Vera-AI v2 with async Qdrant, singleton pattern, monthly curation, and configurable UID/GID/TZ
Features: - AsyncQdrantClient for non-blocking Qdrant operations - Singleton pattern for QdrantService - Monthly full curation (day 1 at 03:00) - Configurable UID/GID for Docker - Timezone support via TZ env var - Configurable log directory (VERA_LOG_DIR) - Volume mounts for config/, prompts/, logs/ - Standard Docker format with .env file Fixes: - Removed unused system_token_budget - Added semantic_score_threshold config - Fixed streaming response handling - Python-based healthcheck (no curl dependency)
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Vera-AI
215
app/proxy_handler.py
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215
app/proxy_handler.py
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# app/proxy_handler.py
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from fastapi import Request
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from fastapi.responses import JSONResponse, StreamingResponse
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import httpx
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import json
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import re
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import logging
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import os
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from pathlib import Path
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from .config import config
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from .singleton import get_qdrant_service
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from .utils import count_tokens, build_augmented_messages
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logger = logging.getLogger(__name__)
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# Debug log directory (configurable via environment)
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# Logs are written to VERA_LOG_DIR or /app/logs by default
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DEBUG_LOG_DIR = Path(os.environ.get("VERA_LOG_DIR", "/app/logs"))
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def clean_message_content(content: str) -> str:
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"""Strip [Memory context] wrapper and extract actual user message."""
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if not content:
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return content
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# Check for OpenJarvis/OpenClaw wrapper
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wrapper_match = re.search(
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r'\[Memory context\].*?- user_msg:\s*(.+?)(?:\n\n|\Z)',
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content, re.DOTALL
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)
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if wrapper_match:
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return wrapper_match.group(1).strip()
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# Also strip timestamp prefixes if present
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ts_match = re.match(r'\[\d{4}-\d{2}-\d{2}[^\]]*\]\s*', content)
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if ts_match:
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return content[ts_match.end():].strip()
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return content
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def debug_log(category: str, message: str, data: dict = None):
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"""Append a debug entry to the daily debug log if debug mode is enabled.
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Logs are written to VERA_LOG_DIR/debug_YYYY-MM-DD.log
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This ensures logs are persisted and easily accessible.
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"""
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if not config.debug:
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return
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from datetime import datetime
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# Create logs directory
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log_dir = DEBUG_LOG_DIR
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log_dir.mkdir(parents=True, exist_ok=True)
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today = datetime.utcnow().strftime("%Y-%m-%d")
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log_path = log_dir / f"debug_{today}.log"
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entry = {
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"timestamp": datetime.utcnow().isoformat() + "Z",
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"category": category,
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"message": message
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}
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if data:
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entry["data"] = data
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with open(log_path, "a") as f:
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f.write(json.dumps(entry) + "\n")
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async def handle_chat_non_streaming(body: dict):
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"""Handle non-streaming chat request."""
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incoming_messages = body.get("messages", [])
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model = body.get("model", "")
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debug_log("INPUT", "Non-streaming chat request", {"messages": incoming_messages})
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# Clean messages
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cleaned_messages = []
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for msg in incoming_messages:
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if msg.get("role") == "user":
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cleaned_content = clean_message_content(msg.get("content", ""))
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cleaned_messages.append({"role": "user", "content": cleaned_content})
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else:
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cleaned_messages.append(msg)
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# Build augmented messages
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augmented_messages = await build_augmented_messages(cleaned_messages)
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debug_log("THOUGHT", "Built augmented messages", {"augmented_count": len(augmented_messages)})
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# Forward to Ollama
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forwarded_body = body.copy()
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forwarded_body["messages"] = augmented_messages
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async with httpx.AsyncClient(timeout=300.0) as client:
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resp = await client.post(f"{config.ollama_host}/api/chat", json=forwarded_body)
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result = resp.json()
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debug_log("OUTPUT", "LLM non-streaming response", {"response": result})
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# Store the Q&A turn
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user_question = ""
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for msg in reversed(incoming_messages):
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if msg.get("role") == "user":
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user_question = clean_message_content(msg.get("content", ""))
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break
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assistant_answer = result.get("message", {}).get("content", "")
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if user_question and assistant_answer:
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qdrant_service = get_qdrant_service()
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try:
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result_id = await qdrant_service.store_qa_turn(user_question, assistant_answer)
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debug_log("STORAGE", "Non-streaming Q&A stored", {"question": user_question, "answer": assistant_answer})
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except Exception as e:
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logger.error(f"[STORE] FAILED: {e}")
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result["model"] = model
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return JSONResponse(content=result)
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async def handle_chat(request: Request):
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"""Handle streaming chat request."""
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body = await request.json()
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incoming_messages = body.get("messages", [])
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model = body.get("model", "")
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debug_log("INPUT", "Streaming chat request", {"messages": incoming_messages})
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# Clean messages
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cleaned_messages = []
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for msg in incoming_messages:
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if msg.get("role") == "user":
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cleaned_content = clean_message_content(msg.get("content", ""))
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cleaned_messages.append({"role": "user", "content": cleaned_content})
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else:
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cleaned_messages.append(msg)
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# Build augmented messages
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augmented_messages = await build_augmented_messages(cleaned_messages)
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debug_log("THOUGHT", "Built augmented messages for streaming", {
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"original_count": len(incoming_messages),
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"augmented_count": len(augmented_messages)
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})
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# Forward to Ollama with streaming
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forwarded_body = body.copy()
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forwarded_body["messages"] = augmented_messages
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headers = dict(request.headers)
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headers.pop("content-length", None)
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headers.pop("Content-Length", None)
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headers.pop("content-type", None)
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headers.pop("Content-Type", None)
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async def stream_response():
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async with httpx.AsyncClient(timeout=300.0) as client:
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resp = await client.post(
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f"{config.ollama_host}/api/chat",
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json=forwarded_body,
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headers=headers
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)
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full_content = ""
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async for chunk in resp.aiter_bytes():
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yield chunk
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for line in chunk.decode().strip().splitlines():
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try:
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data = json.loads(line)
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if "message" in data and "content" in data["message"]:
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full_content += data["message"]["content"]
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except json.JSONDecodeError:
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pass
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debug_log("OUTPUT", "LLM streaming response complete", {
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"content_length": len(full_content)
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})
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# Store Q&A turn
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user_question = ""
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for msg in reversed(incoming_messages):
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if msg.get("role") == "user":
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user_question = clean_message_content(msg.get("content", ""))
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break
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if user_question and full_content:
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qdrant_service = get_qdrant_service()
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try:
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result_id = await qdrant_service.store_qa_turn(user_question, full_content)
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logger.info(f"[STORE] Success! ID={result_id[:8]}, Q={len(user_question)} chars")
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except Exception as e:
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logger.error(f"[STORE] FAILED: {type(e).__name__}: {e}")
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return StreamingResponse(stream_response(), media_type="application/x-ndjson")
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async def forward_to_ollama(request: Request, path: str):
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"""Forward request to Ollama transparently."""
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body = await request.body()
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headers = dict(request.headers)
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headers.pop("content-length", None)
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headers.pop("Content-Length", None)
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async with httpx.AsyncClient(timeout=300.0) as client:
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resp = await client.request(
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method=request.method,
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url=f"{config.ollama_host}{path}",
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content=body,
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headers=headers
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)
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return resp
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