gramps
d01dd3b761
- config.py: all constants, env vars, limits, skill registry, profiles - db.py: schema init, connection factory, skill state helpers - security.py: PIN hashing, audit logging, rate limiting, CSRF, request helpers - auth.py: session management, PIN verify, auth routes - memory.py: FTS5 CRUD + remember/forget command processing - search.py: SearXNG integration, perplexity scoring, refusal/hedge detection - gpu.py: rocm-smi stats - rag.py: Qdrant vector search + system prompt assembly - routers/: conversations, memories, models, presets, profile, settings, skills, chat, search - app.py: slim entry point, middleware, router registration only Bumps to v1.9.0
109 lines
4.7 KiB
Python
109 lines
4.7 KiB
Python
"""JarvisChat routers - /api/search explicit search endpoint."""
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import json
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import logging
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import uuid
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from datetime import datetime, timezone
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import httpx
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from fastapi import APIRouter, HTTPException, Request
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from fastapi.responses import StreamingResponse
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from config import DEFAULT_MODEL, LLAMA_SERVER_BASE, MAX_SEARCH_QUERY_CHARS
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from db import get_db
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from search import query_searxng, format_search_results
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from routers.chat import parse_llama_stream_chunk
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from security import read_json_body, log_incident, BODY_LIMIT_CHAT_BYTES
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log = logging.getLogger("jarvischat")
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router = APIRouter()
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@router.post("/api/search")
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async def explicit_search(request: Request):
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body = await read_json_body(request, BODY_LIMIT_CHAT_BYTES)
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query = body.get("query", "").strip()
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if len(query) > MAX_SEARCH_QUERY_CHARS:
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raise HTTPException(status_code=413, detail="Search query is too long")
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conv_id = body.get("conversation_id")
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model = body.get("model", DEFAULT_MODEL)
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if not query:
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raise HTTPException(status_code=400, detail="Empty query")
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db = get_db()
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now = datetime.now(timezone.utc).isoformat()
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if not conv_id:
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conv_id = str(uuid.uuid4())
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title = f"🔍 {query[:70]}..." if len(query) > 70 else f"🔍 {query}"
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db.execute("INSERT INTO conversations (id, title, model, created_at, updated_at) VALUES (?, ?, ?, ?, ?)",
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(conv_id, title, model, now, now))
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else:
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db.execute("UPDATE conversations SET updated_at = ? WHERE id = ?", (now, conv_id))
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db.execute("INSERT INTO messages (conversation_id, role, content, created_at) VALUES (?, ?, ?, ?)",
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(conv_id, "user", f"🔍 {query}", now))
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db.commit()
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db.close()
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async def stream_search():
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yield f"data: {json.dumps({'conversation_id': conv_id, 'searching': True})}\n\n"
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results = await query_searxng(query, max_results=5)
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if not results:
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error_msg = "No search results found."
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yield f"data: {json.dumps({'token': error_msg, 'conversation_id': conv_id})}\n\n"
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db2 = get_db()
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db2.execute("INSERT INTO messages (conversation_id, role, content, created_at) VALUES (?, ?, ?, ?)",
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(conv_id, "assistant", error_msg, datetime.now(timezone.utc).isoformat()))
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db2.commit()
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db2.close()
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yield f"data: {json.dumps({'done': True, 'conversation_id': conv_id})}\n\n"
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return
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yield f"data: {json.dumps({'search_results': len(results), 'conversation_id': conv_id})}\n\n"
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search_context = format_search_results(results)
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messages = [
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{"role": "system", "content": f"You have access to current web data. Answer directly using ONLY the data below. Be concise. No apologies. No disclaimers.\n\n{search_context}"},
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{"role": "user", "content": query},
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]
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full_response = []
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async with httpx.AsyncClient() as client:
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try:
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async with client.stream(
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"POST", f"{LLAMA_SERVER_BASE}/v1/chat/completions",
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json={"model": model, "messages": messages, "stream": True},
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timeout=httpx.Timeout(300.0, connect=10.0),
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) as resp:
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async for line in resp.aiter_lines():
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if line.strip():
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token, done, _ = parse_llama_stream_chunk(line)
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if token:
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full_response.append(token)
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yield f"data: {json.dumps({'token': token, 'conversation_id': conv_id})}\n\n"
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if done:
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break
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except Exception as e:
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incident_key = log_incident("search_summarization_stream",
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message="Stream failure during explicit search summarization",
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request=request, exc=e)
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yield f"data: {json.dumps({'error': 'Search summarization could not complete right now.', 'error_key': incident_key})}\n\n"
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return
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summary = "".join(full_response)
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saved_msg = f"{summary}\n\n---\n*🔍 Web search results*"
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db2 = get_db()
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db2.execute("INSERT INTO messages (conversation_id, role, content, created_at) VALUES (?, ?, ?, ?)",
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(conv_id, "assistant", saved_msg, datetime.now(timezone.utc).isoformat()))
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db2.commit()
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db2.close()
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yield f"data: {json.dumps({'raw_results': results, 'conversation_id': conv_id})}\n\n"
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yield f"data: {json.dumps({'done': True, 'conversation_id': conv_id, 'searched': True})}\n\n"
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return StreamingResponse(stream_search(), media_type="text/event-stream")
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