- Add backend/frontend Dockerfiles, nginx reverse proxy config, and docker-compose.yml to run the app on AWS Lightsail; document the setup in docs/deploy-lightsail.md. Fix a .gitignore path anchoring bug that left backend/knowledge_base/web_search/ untracked instead of ignored. - Summarize web search results into structured Markdown via LLM before saving them to the local knowledge base, and support regenerating existing entries from their source URLs (backend/app/core/memory_kb). - Downgrade vite to ^6 to match @vitejs/plugin-vue's peer dependency range, fixing a failing `npm ci` (found while testing the Docker build). Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
177 lines
5.7 KiB
Python
177 lines
5.7 KiB
Python
import asyncio
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import uuid
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from datetime import datetime, timezone
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from fastapi import APIRouter, Depends, HTTPException
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from fastapi.responses import StreamingResponse
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from pydantic import BaseModel
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from sqlalchemy.orm import Session as DBSession
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from app.db.database import get_db
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from app.db.models import Session, Message
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from app.core.memory_kb import memory as mem
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from app.core import llm
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from app.core.memory_kb import knowledge
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from app.core.memory_kb import local_knowledge
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from app.core.search.websearch import WEB_SEARCH_TOOL
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from app.core.config import DEFAULT_MODEL
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from app.core.memory_kb.tagging import tag_message_by_id
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from app.core.memory_kb.word_memory import record_message
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router = APIRouter(prefix="/chat", tags=["chat"])
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class ChatRequest(BaseModel):
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session_id: str | None = None
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message: str
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model: str | None = None
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class SessionResponse(BaseModel):
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id: str
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title: str
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model: str
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updated_at: datetime
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class Config:
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from_attributes = True
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class MessageResponse(BaseModel):
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id: int
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role: str
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content: str
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created_at: datetime
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tags: list[str] = []
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class Config:
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from_attributes = True
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@router.get("/sessions", response_model=list[SessionResponse])
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def list_sessions(db: DBSession = Depends(get_db)):
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return db.query(Session).order_by(Session.updated_at.desc()).all()
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@router.get("/sessions/{session_id}/messages", response_model=list[MessageResponse])
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def get_messages(session_id: str, db: DBSession = Depends(get_db)):
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session = db.get(Session, session_id)
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if not session:
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raise HTTPException(status_code=404, detail="Session not found")
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return [
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MessageResponse(
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id=m.id,
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role=m.role,
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content=m.content,
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created_at=m.created_at,
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tags=[t.tag for t in m.tags],
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)
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for m in session.messages
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]
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@router.delete("/sessions/{session_id}", status_code=204)
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def delete_session(session_id: str, db: DBSession = Depends(get_db)):
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session = db.get(Session, session_id)
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if not session:
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raise HTTPException(status_code=404, detail="Session not found")
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db.delete(session)
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db.commit()
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@router.post("/knowledge/regenerate")
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async def regenerate_knowledge(db: DBSession = Depends(get_db)):
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"""既存のKnowledgeEntryを出典URLから取得し直し、構造化要約に再生成する(一括実行用)。"""
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count = await knowledge.regenerate_from_sources(db)
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return {"regenerated": count}
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@router.post("/stream")
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async def stream_chat(req: ChatRequest, db: DBSession = Depends(get_db)):
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# セッション取得 or 新規作成
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if req.session_id:
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session = db.get(Session, req.session_id)
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if not session:
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raise HTTPException(status_code=404, detail="Session not found")
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else:
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session = Session(
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id=str(uuid.uuid4()),
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title=req.message[:40],
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model=req.model or DEFAULT_MODEL,
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)
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db.add(session)
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db.commit()
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# ユーザーメッセージを保存
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user_msg = Message(session_id=session.id, role="user", content=req.message)
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db.add(user_msg)
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db.commit()
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db.refresh(user_msg)
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record_message(req.message)
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asyncio.create_task(tag_message_by_id(user_msg.id))
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context = await mem.get_context_messages(db, session.id)
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async def generate():
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import json as _json
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def sse(payload: dict) -> str:
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return f"data: {_json.dumps(payload, ensure_ascii=False)}\n\n"
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# セッションIDをまず返す(新規セッション時にフロントへ通知)
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yield sse({"type": "session_id", "value": session.id})
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used_model = req.model or session.model
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full_response = []
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try:
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resolved_context, searches = await llm.resolve_web_search(
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context, model=used_model, session_id=session.id,
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)
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if searches:
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sources = [
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{"title": r["title"], "url": r["url"]}
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for s in searches for r in s["results"]
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]
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yield sse({"type": "sources", "value": sources})
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for s in searches:
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summary = await knowledge.summarize_results(s["query"], s["results"])
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if summary:
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knowledge.save_knowledge(db, s["query"], summary, s["results"])
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local_knowledge.save_search_result(s["query"], summary, s["results"])
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search_tools = [WEB_SEARCH_TOOL] if len(resolved_context) > len(context) else None
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async for chunk in llm.stream_chat(
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resolved_context, model=used_model, session_id=session.id, tools=search_tools,
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):
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full_response.append(chunk)
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yield sse({"type": "chunk", "value": chunk})
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# アシスタントメッセージを保存
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assistant_content = "".join(full_response)
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assistant_msg = Message(session_id=session.id, role="assistant", content=assistant_content)
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db.add(assistant_msg)
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session.updated_at = datetime.now(timezone.utc)
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db.commit()
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db.refresh(assistant_msg)
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record_message(assistant_content)
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asyncio.create_task(tag_message_by_id(assistant_msg.id))
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# メモリ要約チェック
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await mem.maybe_summarize(db, session)
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yield sse({"type": "done"})
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except Exception as e:
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yield sse({"type": "error", "value": str(e)})
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return StreamingResponse(
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generate(),
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media_type="text/event-stream",
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headers={
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"Cache-Control": "no-cache",
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"X-Accel-Buffering": "no",
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"Connection": "keep-alive",
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},
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)
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