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}