cc1efa7a21
- Add COMPLETIONS_API_KEY to config.py (env var + auto-generated fallback) - Fix perplexity auto-search: upstream sends logprobs=true, parse_llama_stream_chunk extracts per-token logprobs, all_logprobs populated during streaming - Fix all /api/models endpoints to target LLAMA_SERVER_BASE (port 8081) not OLLAMA_BASE - Fix RAG embedding endpoint URL from port 11434 (Ollama) to 8081 (llama-server) - Correct misleading error messages: 'inference server' not 'Ollama' - Remove raw_results leak from SSE event stream in /api/search - Fix weather query extractor: pattern-match instead of unconditional suffix append - Escape FTS5 operator keywords (AND/OR/NOT/NEAR) in memory search - Move auth.py BODY_LIMIT_DEFAULT_BYTES imports to module level - Change RAG injection log level from warning to info - Fix all 8 test files after modular refactor (rewire imports from correct modules) - Update AGENTS.md and README.md to reflect v1.8.0 changes
81 lines
3.1 KiB
Python
81 lines
3.1 KiB
Python
"""
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JarvisChat - RAG pipeline: Qdrant vector search + system prompt assembly.
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"""
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import logging
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import httpx
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from db import get_db, get_setting, list_skills_with_state, format_active_skills_prompt
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from memory import search_memories
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from config import MAX_SKILL_PROMPT_CHARS
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log = logging.getLogger("jarvischat")
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QDRANT_URL = "http://192.168.50.108:6333"
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EMBED_URL = "http://192.168.50.108:8081"
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EMBED_MODEL = "mxbai-embed-large"
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RAG_COLLECTION = "jarvis_rag"
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RAG_SCORE_THRESHOLD = 0.25
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async def query_rag(query: str, limit: int = 3) -> list:
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try:
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async with httpx.AsyncClient() as client:
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embed_resp = await client.post(
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f"{EMBED_URL}/api/embeddings",
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json={"model": EMBED_MODEL, "prompt": query},
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timeout=10.0,
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)
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if embed_resp.status_code != 200:
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return []
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vector = embed_resp.json()["embedding"]
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search_resp = await client.post(
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f"{QDRANT_URL}/collections/{RAG_COLLECTION}/points/search",
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json={"vector": vector, "limit": limit, "with_payload": True},
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timeout=10.0,
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)
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if search_resp.status_code != 200:
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return []
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return search_resp.json().get("result", [])
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except Exception as e:
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log.warning(f"RAG query error: {e}")
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return []
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async def build_system_prompt(db, extra_prompt: str = "", user_message: str = "") -> str:
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parts = []
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settings = {row["key"]: row["value"] for row in db.execute("SELECT key, value FROM settings").fetchall()}
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if settings.get("profile_enabled", "true") == "true":
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profile = db.execute("SELECT content FROM profile WHERE id = 1").fetchone()
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if profile and profile["content"].strip():
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parts.append(profile["content"].strip())
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if settings.get("memory_enabled", "true") == "true" and user_message:
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memories = search_memories(user_message, limit=5)
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if memories:
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memory_lines = [f"- {m['fact']}" for m in memories]
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parts.append("## Relevant Context from Memory\n" + "\n".join(memory_lines))
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log.debug(f"Injected {len(memories)} memories into context")
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if user_message:
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try:
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rag_results = await query_rag(user_message)
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if rag_results:
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rag_lines = [r["payload"]["text"] for r in rag_results if r["score"] > RAG_SCORE_THRESHOLD]
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if rag_lines:
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parts.append("## Retrieved Context\n" + "\n\n---\n\n".join(rag_lines))
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log.info(f"RAG injected {len(rag_lines)} chunks into context")
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except Exception as e:
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log.warning(f"RAG injection error: {e}")
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if settings.get("skills_enabled", "true") == "true":
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active_skills = [s for s in list_skills_with_state(db) if s["enabled"]]
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if active_skills:
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parts.append(format_active_skills_prompt(active_skills))
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if extra_prompt and extra_prompt.strip():
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parts.append(extra_prompt.strip())
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return "\n\n---\n\n".join(parts) if parts else ""
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