9974ce8dd9
Chat commands (intercept before LLM):
"запомни что X" / "remember X" — saves with importance 0.9
"забудь про X" / "forget X" — deletes by vector similarity
REST API:
POST /v1/memories — add memory manually
PATCH /v1/memories/{id} — update content or importance
DELETE /v1/memories/{id} — delete by id (existing)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
295 lines
12 KiB
Python
295 lines
12 KiB
Python
"""
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code-memory: OpenAI-compatible proxy with GraphRAG + persistent memory.
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Endpoints:
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POST /v1/chat/completions — drop-in Ollama replacement with memory
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POST /v1/index — index a code repository
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GET /v1/memories — list memories
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DELETE /v1/memories/{id} — delete a memory
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GET /health — health check
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"""
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import json, os, asyncio
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from contextlib import asynccontextmanager
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from typing import AsyncIterator
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import asyncpg
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from fastapi import FastAPI, HTTPException, BackgroundTasks
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from fastapi.responses import StreamingResponse
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from pydantic import BaseModel, Field
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from dotenv import load_dotenv
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load_dotenv()
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import db as _db
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import memory as mem
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import graph as gph
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import ollama_client as oll
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from apscheduler.schedulers.asyncio import AsyncIOScheduler
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# ── lifespan ──────────────────────────────────────────────────────────────────
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scheduler = AsyncIOScheduler()
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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await _db.init_db()
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# CMA consolidation runs every 6 hours
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scheduler.add_job(mem.consolidate_memories, "interval", hours=6,
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kwargs={"user_id": "default"})
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scheduler.start()
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yield
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scheduler.shutdown()
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app = FastAPI(title="code-memory", lifespan=lifespan)
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# ── models ────────────────────────────────────────────────────────────────────
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class Message(BaseModel):
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role: str
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content: str
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class ChatRequest(BaseModel):
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model: str = Field(default=None)
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messages: list[Message]
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stream: bool = False
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session_id: str = "default"
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user_id: str = "default"
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project_id: str | None = None
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class IndexRequest(BaseModel):
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project_id: str
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root_dir: str
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extensions: list[str] = [".py"]
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# ── models list endpoint ──────────────────────────────────────────────────────
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@app.get("/v1/models")
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async def list_models():
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return {
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"object": "list",
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"data": [{
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"id": "code-memory",
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"object": "model",
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"owned_by": "code-memory",
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"created": 1700000000,
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}]
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}
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# ── context assembly ──────────────────────────────────────────────────────────
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async def build_context(request: ChatRequest) -> list[dict]:
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user_msg = next((m.content for m in reversed(request.messages)
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if m.role == "user"), "")
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# 1. Relevant archival memories
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memories = await mem.search_memories(user_msg, user_id=request.user_id, limit=6)
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mem_block = ""
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if memories:
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mem_block = "## Relevant memories\n" + "\n".join(
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f"- [{m['memory_type']}] {m['content']}" for m in memories
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if m.get("score", 0) > 0.4
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)
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# 2. GraphRAG code context
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code_block = ""
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if request.project_id:
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ctx = await gph.graphrag_context(user_msg, request.project_id)
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if ctx:
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code_block = f"## Code architecture context\n{ctx}"
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# 3. Conversation summary (if exists)
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summary = await mem.get_summary(request.session_id)
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summary_block = f"## Earlier conversation summary\n{summary}" if summary else ""
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# 4. Assemble system prompt
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base_system = (
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"You are an expert coding assistant with deep knowledge of the project architecture. "
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"Use the provided memory and code context to give precise, informed answers. "
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"Always reason about the full architecture, not just isolated snippets."
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)
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context_parts = [p for p in [base_system, summary_block, mem_block, code_block] if p]
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system_content = "\n\n".join(context_parts)
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# 5. Build messages: system + recent history + current
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recent = await mem.get_recent_turns(request.session_id, limit=20)
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messages = [{"role": "system", "content": system_content}]
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messages += [{"role": r["role"], "content": r["content"]} for r in recent]
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# add current user messages (skip if already in history)
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for m in request.messages:
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if not any(h["role"] == m.role and h["content"] == m.content for h in recent[-2:]):
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messages.append({"role": m.role, "content": m.content})
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return messages
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# ── chat endpoint ─────────────────────────────────────────────────────────────
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FORGET_PREFIXES = ("забудь про ", "забудь ", "forget ")
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REMEMBER_PREFIXES = ("запомни что ", "запомни ", "remember ")
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async def handle_memory_command(user_content: str, user_id: str) -> str | None:
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low = user_content.lower().strip()
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for prefix in FORGET_PREFIXES:
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if low.startswith(prefix):
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topic = user_content[len(prefix):].strip()
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pool = await _db.get_pool()
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vec = await oll.embed(topic)
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from memory import _vec_str
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async with pool.acquire() as conn:
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deleted = await conn.fetchval("""
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DELETE FROM memories
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WHERE user_id=$1
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AND 1-(embedding<=>$2::vector) > 0.60
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RETURNING content
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""", user_id, _vec_str(vec))
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if deleted:
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return f"Забыл: «{deleted}»"
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return f"Не нашёл ничего похожего на «{topic}»"
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for prefix in REMEMBER_PREFIXES:
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if low.startswith(prefix):
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fact = user_content[len(prefix):].strip()
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await mem.store_memory(fact, user_id=user_id, memory_type="manual", importance=0.9)
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return f"Запомнил: «{fact}»"
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return None
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@app.post("/v1/chat/completions")
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async def chat_completions(req: ChatRequest, bg: BackgroundTasks):
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user_content = next((m.content for m in reversed(req.messages)
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if m.role == "user"), "")
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# intercept memory commands before building context
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cmd_response = await handle_memory_command(user_content, req.user_id)
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if cmd_response:
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return {
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"id": "cmpl-cmd",
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"object": "chat.completion",
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"model": req.model or os.getenv("CHAT_MODEL"),
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"choices": [{"index": 0,
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"message": {"role": "assistant", "content": cmd_response},
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"finish_reason": "stop"}]
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}
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messages = await build_context(req)
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# use real Ollama model, not our proxy name
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ollama_model = None if req.model == "code-memory" else req.model
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if req.stream:
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collected: list[str] = []
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async def gen() -> AsyncIterator[bytes]:
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try:
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async for chunk in oll.chat_stream(messages, model=ollama_model):
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collected.append(chunk)
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data = json.dumps({"choices": [{"delta": {"content": chunk},
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"finish_reason": None}]})
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yield f"data: {data}\n\n".encode()
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yield b"data: [DONE]\n\n"
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except Exception as e:
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yield f"data: {json.dumps({'error': str(e)})}\n\n".encode()
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async def _save_stream():
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response_text = "".join(collected)
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await mem.save_turn(req.session_id, "user", user_content)
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await mem.save_turn(req.session_id, "assistant", response_text)
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await mem.extract_and_store(user_content, user_id=req.user_id)
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await mem.extract_and_store(response_text, user_id=req.user_id)
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await mem.summarize_old_turns(req.session_id)
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bg.add_task(_save_stream)
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return StreamingResponse(gen(), media_type="text/event-stream")
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response_text = await oll.chat(messages, model=ollama_model)
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# background: save turns + extract facts
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async def _save():
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await mem.save_turn(req.session_id, "user", user_content)
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await mem.save_turn(req.session_id, "assistant", response_text)
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await mem.extract_and_store(user_content, user_id=req.user_id)
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await mem.extract_and_store(response_text, user_id=req.user_id)
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await mem.summarize_old_turns(req.session_id)
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bg.add_task(_save)
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return {
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"id": "cmpl-1",
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"object": "chat.completion",
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"model": req.model or os.getenv("CHAT_MODEL"),
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"choices": [{"index": 0,
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"message": {"role": "assistant", "content": response_text},
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"finish_reason": "stop"}]
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}
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# ── index endpoint ────────────────────────────────────────────────────────────
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@app.post("/v1/index")
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async def index_code(req: IndexRequest, bg: BackgroundTasks):
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if not os.path.isdir(req.root_dir):
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raise HTTPException(404, f"Directory not found: {req.root_dir}")
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bg.add_task(gph.index_project, req.project_id, req.root_dir, req.extensions)
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return {"status": "indexing started", "project_id": req.project_id}
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# ── memory endpoints ──────────────────────────────────────────────────────────
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@app.get("/v1/memories")
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async def list_memories(user_id: str = "default", limit: int = 50):
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pool = await _db.get_pool()
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async with pool.acquire() as conn:
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rows = await conn.fetch("""
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SELECT id, content, importance, memory_type, access_count,
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last_accessed, created_at
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FROM memories WHERE user_id=$1
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AND (metadata->>'archived') IS NULL
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ORDER BY importance DESC, last_accessed DESC LIMIT $2
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""", user_id, limit)
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return [dict(r) for r in rows]
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@app.post("/v1/memories")
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async def create_memory(body: dict, user_id: str = "default"):
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content = body.get("content", "").strip()
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if not content:
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raise HTTPException(400, "content required")
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importance = float(body.get("importance", 0.9))
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memory_type = body.get("memory_type", "manual")
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await mem.store_memory(content, user_id=user_id,
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memory_type=memory_type, importance=importance)
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return {"status": "stored", "content": content}
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@app.patch("/v1/memories/{memory_id}")
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async def update_memory(memory_id: str, body: dict):
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import uuid as _uuid
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pool = await _db.get_pool()
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uid = _uuid.UUID(memory_id)
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async with pool.acquire() as conn:
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row = await conn.fetchrow("SELECT * FROM memories WHERE id=$1", uid)
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if not row:
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raise HTTPException(404, "memory not found")
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new_content = body.get("content", row["content"])
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new_importance = float(body.get("importance", row["importance"]))
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if new_content != row["content"]:
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vec = await oll.embed(new_content)
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from memory import _vec_str
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await conn.execute("""
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UPDATE memories SET content=$1, embedding=$2::vector,
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importance=$3, last_accessed=NOW()
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WHERE id=$4
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""", new_content, _vec_str(vec), new_importance, uid)
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else:
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await conn.execute("""
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UPDATE memories SET importance=$1, last_accessed=NOW()
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WHERE id=$2
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""", new_importance, uid)
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return {"updated": memory_id}
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@app.delete("/v1/memories/{memory_id}")
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async def delete_memory(memory_id: str):
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pool = await _db.get_pool()
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async with pool.acquire() as conn:
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await conn.execute("DELETE FROM memories WHERE id=$1",
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__import__("uuid").UUID(memory_id))
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return {"deleted": memory_id}
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@app.get("/health")
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async def health():
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pool = await _db.get_pool()
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async with pool.acquire() as conn:
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await conn.fetchval("SELECT 1")
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return {"status": "ok", "db": "connected"}
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