145 lines
4.4 KiB
Python
145 lines
4.4 KiB
Python
from __future__ import annotations
|
|
|
|
from fastapi import FastAPI, HTTPException
|
|
from pydantic import BaseModel
|
|
from typing import List, Dict, Any
|
|
|
|
from .config import settings
|
|
from .ollama_client import OllamaClient
|
|
from .index_store import JsonlIndex
|
|
|
|
|
|
app = FastAPI(title="Local AI Server", version="0.1.0")
|
|
ollama = OllamaClient(settings.ollama_host)
|
|
index = JsonlIndex(settings.index_path)
|
|
|
|
|
|
class ChatRequest(BaseModel):
|
|
model: str | None = None
|
|
messages: List[Dict[str, str]]
|
|
use_rag: bool = True
|
|
top_k: int = 5
|
|
force_boost: bool = False
|
|
options: Dict[str, Any] | None = None
|
|
|
|
|
|
class SearchRequest(BaseModel):
|
|
query: str
|
|
top_k: int = 5
|
|
|
|
class UpsertRow(BaseModel):
|
|
id: str
|
|
text: str
|
|
source: str | None = None
|
|
|
|
class UpsertRequest(BaseModel):
|
|
rows: List[UpsertRow]
|
|
embed: bool = True
|
|
model: str | None = None
|
|
batch: int = 16
|
|
|
|
|
|
@app.get("/health")
|
|
def health() -> Dict[str, Any]:
|
|
return {
|
|
"status": "ok",
|
|
"base_model": settings.base_model,
|
|
"boost_model": settings.boost_model,
|
|
"embedding_model": settings.embedding_model,
|
|
"index_loaded": len(index.rows) if index else 0,
|
|
}
|
|
|
|
|
|
@app.post("/search")
|
|
def search(req: SearchRequest) -> Dict[str, Any]:
|
|
if not index.rows:
|
|
return {"results": []}
|
|
qvec = ollama.embeddings(settings.embedding_model, req.query)
|
|
results = index.search(qvec, top_k=req.top_k)
|
|
return {
|
|
"results": [
|
|
{"id": r.id, "score": float(score), "text": r.text[:400], "source": r.source}
|
|
for r, score in results
|
|
]
|
|
}
|
|
|
|
|
|
@app.post("/chat")
|
|
def chat(req: ChatRequest) -> Dict[str, Any]:
|
|
model = req.model
|
|
if not model:
|
|
# 라우팅: 메시지 길이/force_boost 기준 간단 분기
|
|
total_chars = sum(len(m.get("content", "")) for m in req.messages)
|
|
model = settings.boost_model if (req.force_boost or total_chars > 2000) else settings.base_model
|
|
|
|
context_docs: List[str] = []
|
|
if req.use_rag and index.rows:
|
|
q = "\n".join([m.get("content", "") for m in req.messages if m.get("role") == "user"]).strip()
|
|
if q:
|
|
qvec = ollama.embeddings(settings.embedding_model, q)
|
|
hits = index.search(qvec, top_k=req.top_k)
|
|
context_docs = [r.text for r, _ in hits]
|
|
|
|
sys_prompt = ""
|
|
if context_docs:
|
|
sys_prompt = (
|
|
"당신은 문서 기반 비서입니다. 제공된 컨텍스트만 신뢰하고, 모르면 모른다고 답하세요.\n\n"
|
|
+ "\n\n".join(f"[DOC {i+1}]\n{t}" for i, t in enumerate(context_docs))
|
|
)
|
|
|
|
messages: List[Dict[str, str]] = []
|
|
if sys_prompt:
|
|
messages.append({"role": "system", "content": sys_prompt})
|
|
messages.extend(req.messages)
|
|
|
|
try:
|
|
resp = ollama.chat(model, messages, stream=False, options=req.options)
|
|
return {"model": model, "response": resp}
|
|
except Exception as e:
|
|
raise HTTPException(status_code=500, detail=str(e))
|
|
|
|
|
|
@app.post("/index/upsert")
|
|
def index_upsert(req: UpsertRequest) -> Dict[str, Any]:
|
|
try:
|
|
if not req.rows:
|
|
return {"added": 0}
|
|
model = req.model or settings.embedding_model
|
|
new_rows = []
|
|
for r in req.rows:
|
|
vec = ollama.embeddings(model, r.text) if req.embed else []
|
|
new_rows.append({
|
|
"id": r.id,
|
|
"text": r.text,
|
|
"vector": vec,
|
|
"source": r.source or "api",
|
|
})
|
|
# convert to IndexRow and append
|
|
from .index_store import IndexRow
|
|
to_append = [IndexRow(**nr) for nr in new_rows]
|
|
added = index.append(to_append)
|
|
return {"added": added}
|
|
except Exception as e:
|
|
raise HTTPException(status_code=500, detail=f"index_upsert_error: {e}")
|
|
|
|
|
|
@app.post("/index/reload")
|
|
def index_reload() -> Dict[str, Any]:
|
|
total = index.reload()
|
|
return {"total": total}
|
|
|
|
|
|
# Paperless webhook placeholder (to be wired with user-provided details)
|
|
class PaperlessHook(BaseModel):
|
|
document_id: int
|
|
title: str | None = None
|
|
tags: List[str] | None = None
|
|
|
|
|
|
@app.post("/paperless/hook")
|
|
def paperless_hook(hook: PaperlessHook) -> Dict[str, Any]:
|
|
# NOTE: 확장 지점 - paperless API를 조회하여 문서 텍스트/메타데이터를 받아
|
|
# scripts/embed_ollama.py와 동일 로직으로 인덱스를 업데이트할 수 있습니다.
|
|
return {"status": "ack", "document_id": hook.document_id}
|
|
|