206 lines
7.7 KiB
Python
206 lines
7.7 KiB
Python
import difflib
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import os
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import re
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import string
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from typing import Any, Callable
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import httpx
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from fastapi import APIRouter, Body, Depends, Header, HTTPException
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from pydantic import BaseModel, Field
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from sqlalchemy.orm import Session
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import embeddings
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from database import get_db
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from models import Avatar, KnowledgeChunk, KnowledgeDoc, QAPair, User
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from responses import ok, fail
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router = APIRouter(tags=["数字分身聊天"])
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CHAT_API_URL = os.getenv("CHAT_API_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1")
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CHAT_API_KEY = os.getenv("CHAT_API_KEY", "")
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CHAT_MODEL = os.getenv("CHAT_MODEL", "qwen-plus")
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MAX_MESSAGE_LENGTH = 4000
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MAX_HISTORY_MESSAGES = 10
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QA_SIMILARITY_THRESHOLD = 0.86
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class ChatMessage(BaseModel):
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role: str = Field(pattern="^(user|assistant)$")
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content: str = Field(min_length=1, max_length=MAX_MESSAGE_LENGTH)
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class ChatIn(BaseModel):
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message: str = Field(min_length=1, max_length=MAX_MESSAGE_LENGTH)
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history: list[ChatMessage] = Field(default_factory=list, max_length=MAX_HISTORY_MESSAGES)
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def _resolve_user(authorization: str | None, db: Session):
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if not authorization:
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return None
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token = authorization.replace("Bearer ", "", 1).replace("bearer ", "", 1).strip()
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return db.query(User).filter(User.app_token == token).first()
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def _require_owned_avatar(db: Session, avatar_id: str, authorization: str | None):
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avatar = db.query(Avatar).filter(Avatar.id == avatar_id).first()
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if not avatar:
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raise HTTPException(status_code=404, detail="分身不存在")
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user = _resolve_user(authorization, db)
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if not user:
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raise HTTPException(status_code=401, detail="未登录")
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if avatar.owner_id and avatar.owner_id != user.huihui_user_id:
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raise HTTPException(status_code=403, detail="无权访问该分身")
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return avatar
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def _normalize_question(value: str) -> str:
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value = (value or "").strip().lower()
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value = re.sub(r"\s+", "", value)
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return value.translate(str.maketrans("", "", string.punctuation + "，。！？；：、（）【】「」‘’“”《》"))
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def _match_standard_qa(question: str, qa_pairs: list[Any]):
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normalized = _normalize_question(question)
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if not normalized:
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return None
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enabled = [qa for qa in qa_pairs if getattr(qa, "enabled", True)]
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for qa in enabled:
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if _normalize_question(getattr(qa, "question", "")) == normalized:
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return qa
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best = None
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best_score = 0.0
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for qa in enabled:
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candidate = _normalize_question(getattr(qa, "question", ""))
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if not candidate:
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continue
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score = difflib.SequenceMatcher(None, normalized, candidate).ratio()
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if score > best_score:
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best, best_score = qa, score
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return best if best_score >= QA_SIMILARITY_THRESHOLD else None
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def _config(avatar: Avatar) -> dict:
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config = getattr(avatar, "config", None) or {}
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return {
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"replyStyle": config.get("replyStyle", "professional"),
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"creativity": max(0, min(100, int(config.get("creativity", 50)))),
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"rigor": max(0, min(100, int(config.get("rigor", 50)))),
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"humor": max(0, min(100, int(config.get("humor", 30)))),
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"responseLength": config.get("responseLength", "medium"),
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"systemPrompt": (config.get("systemPrompt", "") or "").strip(),
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}
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def _build_prompt(avatar: Avatar, history: list[Any], question: str, knowledge_hits: list[dict]) -> list[dict]:
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config = _config(avatar)
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knowledge = "\n".join(
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f"[{hit.get('filename', '知识库')}] {hit.get('snippet', '')}"
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for hit in knowledge_hits
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if hit.get("snippet")
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)
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system = (
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"你是用户的专属数字分身。请基于已提供的知识库回答，不要编造事实；"
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f"回复风格：{config['replyStyle']}；严谨度：{config['rigor']}/100；"
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f"幽默感：{config['humor']}/100；回复长度：{config['responseLength']}。"
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)
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if config["systemPrompt"]:
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system += f"\n额外系统提示词：{config['systemPrompt']}"
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if knowledge:
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system += f"\n以下是可参考的知识库内容：\n{knowledge}"
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messages = [{"role": "system", "content": system}]
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for item in history[-MAX_HISTORY_MESSAGES:]:
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messages.append({"role": item.role, "content": item.content} if hasattr(item, "role") else item)
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messages.append({"role": "user", "content": question.strip()})
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return messages
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def _search_knowledge(db: Session, avatar_id: str, question: str, top_k: int = 5) -> list[dict]:
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chunks = db.query(KnowledgeChunk).filter(KnowledgeChunk.avatar_id == avatar_id).all()
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if not chunks:
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return []
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qvec = embeddings.embed([question])[0]
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scored = []
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for chunk in chunks:
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try:
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vector = __import__("json").loads(chunk.vector)
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except Exception:
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continue
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scored.append((embeddings.cosine(qvec, vector), chunk))
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scored.sort(key=lambda item: item[0], reverse=True)
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results = []
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for score, chunk in scored[: max(1, top_k)]:
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doc = db.query(KnowledgeDoc).filter(KnowledgeDoc.id == chunk.doc_id).first()
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results.append({
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"docId": chunk.doc_id,
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"filename": doc.filename if doc else "",
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"fileType": doc.file_type if doc else "",
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"snippet": chunk.content[:120] + ("…" if len(chunk.content) > 120 else ""),
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"score": round(score, 4),
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})
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return results
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def _call_qwen(messages: list[dict], temperature: float) -> str:
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if not CHAT_API_KEY:
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raise RuntimeError("Qwen 模型服务未配置 CHAT_API_KEY")
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url = f"{CHAT_API_URL.rstrip('/')}/chat/completions"
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payload = {
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"model": CHAT_MODEL,
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"messages": messages,
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"temperature": temperature,
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}
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try:
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response = httpx.post(
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url,
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headers={"Authorization": f"Bearer {CHAT_API_KEY}"},
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json=payload,
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timeout=30,
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)
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response.raise_for_status()
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data = response.json()
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answer = data.get("choices", [{}])[0].get("message", {}).get("content", "")
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except (httpx.HTTPError, ValueError, KeyError, IndexError) as exc:
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raise RuntimeError("Qwen 模型服务暂时不可用") from exc
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if not isinstance(answer, str) or not answer.strip():
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raise RuntimeError("Qwen 模型没有返回有效回答")
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return answer.strip()
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def _resolve_reply(
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db: Session,
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avatar: Avatar,
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question: str,
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history: list[Any],
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*,
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qa_pairs: list[Any] | None = None,
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search_fn: Callable[..., list[dict]] | None = None,
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model_client: Callable[..., str] | None = None,
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) -> dict:
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if qa_pairs is None:
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qa_pairs = db.query(QAPair).filter(QAPair.avatar_id == avatar.id).all()
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matched = _match_standard_qa(question, qa_pairs)
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if matched:
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return {"answer": matched.answer, "source": "qa", "references": []}
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search_fn = search_fn or (lambda query, avatar_id: _search_knowledge(db, avatar_id, query))
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hits = search_fn(question, avatar.id)
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messages = _build_prompt(avatar, history, question, hits)
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config = _config(avatar)
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temperature = 0.2 + config["creativity"] / 100 * 0.6
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model_client = model_client or _call_qwen
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answer = model_client(messages=messages, temperature=temperature)
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return {
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"answer": answer,
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"source": "knowledge" if hits else "qwen",
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"references": hits,
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}
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@router.post("/avatar/{avatar_id}/chat")
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def chat(avatar_id: str, body: ChatIn = Body(...), authorization: str = Header(None), db: Session = Depends(get_db)):
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avatar = _require_owned_avatar(db, avatar_id, authorization)
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try:
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return ok(_resolve_reply(db, avatar, body.message, body.history))
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except RuntimeError as exc:
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return fail(str(exc), code=502)
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