feat: add avatar chat and knowledge workflow
This commit is contained in:
stefanfeng
205
digital-avatar-app/backend/routers/chat.py
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205
digital-avatar-app/backend/routers/chat.py
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@@ -0,0 +1,205 @@
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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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278
digital-avatar-app/backend/routers/knowledge.py
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278
digital-avatar-app/backend/routers/knowledge.py
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@@ -0,0 +1,278 @@
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import os
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import json
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import uuid
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from datetime import datetime, timezone
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from fastapi import APIRouter, UploadFile, File, Depends, Header, HTTPException
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from pydantic import BaseModel
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from sqlalchemy.orm import Session
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from database import get_db
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from models import KnowledgeDoc, QAPair, KnowledgeChunk, Avatar, User
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from responses import ok, fail
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import embeddings
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router = APIRouter()
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BASE_DIR = os.path.dirname(os.path.abspath(__file__))
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UPLOAD_DIR = os.path.join(BASE_DIR, "uploads")
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os.makedirs(UPLOAD_DIR, exist_ok=True)
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ALLOWED_EXT = {".md", ".txt", ".pdf", ".doc", ".docx", ".xlsx"}
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MAX_UPLOAD_BYTES = 10 * 1024 * 1024
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class QAIn(BaseModel):
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question: str = ""
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answer: str = ""
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enabled: bool = True
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class EnabledIn(BaseModel):
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enabled: bool = True
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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="avatar not found")
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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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# ---------------- Documents ----------------
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@router.get("/avatar/{avatar_id}/knowledge/docs")
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def list_docs(avatar_id: str, authorization: str = Header(None), db: Session = Depends(get_db)):
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_require_owned_avatar(db, avatar_id, authorization)
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docs = (
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db.query(KnowledgeDoc)
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.filter(KnowledgeDoc.avatar_id == avatar_id)
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.order_by(KnowledgeDoc.created_at.desc())
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.all()
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)
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return ok([d.to_dict() for d in docs])
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@router.post("/avatar/{avatar_id}/knowledge/docs")
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async def upload_doc(avatar_id: str, file: UploadFile = File(...), authorization: str = Header(None), db: Session = Depends(get_db)):
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_require_owned_avatar(db, avatar_id, authorization)
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ext = os.path.splitext(file.filename or "")[1].lower()
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if ext not in ALLOWED_EXT:
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return fail(f"不支持的文件类型：{ext or '空'}，仅支持 md/txt/pdf/doc/docx/xlsx", code=400)
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avatar_dir = os.path.join(UPLOAD_DIR, avatar_id)
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os.makedirs(avatar_dir, exist_ok=True)
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stored = f"{uuid.uuid4().hex}{ext}"
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path = os.path.join(avatar_dir, stored)
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content = await file.read()
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if len(content) > MAX_UPLOAD_BYTES:
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return fail("文件不能超过 10MB", code=400)
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with open(path, "wb") as f:
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f.write(content)
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doc = KnowledgeDoc(
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avatar_id=avatar_id,
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filename=file.filename,
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file_type=ext.lstrip("."),
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file_size=len(content),
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file_url=f"/api/files/{avatar_id}/{stored}",
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status="parsing",
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)
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db.add(doc)
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db.commit()
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db.refresh(doc)
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# 向量化：抽取文本 -> 分块 -> 调第三方/本地嵌入 -> 存切片
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try:
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text = embeddings.extract_text(path, ext)
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chunks = embeddings.chunk_text(text)
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if chunks:
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vectors = embeddings.embed(chunks)
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for i, (c, v) in enumerate(zip(chunks, vectors)):
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db.add(
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KnowledgeChunk(
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doc_id=doc.id,
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avatar_id=avatar_id,
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content=c,
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vector=json.dumps(v),
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chunk_index=i,
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embedding_model=embeddings.MODEL,
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)
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)
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doc.vectorized = True
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doc.embedding_model = embeddings.MODEL
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doc.chunk_count = len(chunks)
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doc.vectorized_at = datetime.now(timezone.utc)
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doc.status = "ready"
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db.commit()
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db.refresh(doc)
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except Exception as e:
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print("vectorize failed:", e)
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doc.status = "ready" # 上传成功但向量化失败，仍可展示
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db.commit()
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db.refresh(doc)
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return ok(doc.to_dict())
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@router.delete("/avatar/{avatar_id}/knowledge/docs/{doc_id}")
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def delete_doc(avatar_id: str, doc_id: str, authorization: str = Header(None), db: Session = Depends(get_db)):
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_require_owned_avatar(db, avatar_id, authorization)
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doc = (
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db.query(KnowledgeDoc)
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.filter(KnowledgeDoc.id == doc_id, KnowledgeDoc.avatar_id == avatar_id)
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.first()
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)
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if not doc:
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return fail("文档不存在", code=404)
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# 级联删除切片
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db.query(KnowledgeChunk).filter(KnowledgeChunk.doc_id == doc_id).delete()
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try:
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fp = os.path.join(UPLOAD_DIR, avatar_id, os.path.basename(doc.file_url))
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if os.path.exists(fp):
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os.remove(fp)
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except Exception:
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pass
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db.delete(doc)
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db.commit()
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return ok({"id": doc_id})
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# ---------------- 向量检索 ----------------
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@router.get("/avatar/{avatar_id}/knowledge/search")
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def search_knowledge(avatar_id: str, q: str = "", top_k: int = 5, authorization: str = Header(None), db: Session = Depends(get_db)):
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_require_owned_avatar(db, avatar_id, authorization)
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q = (q or "").strip()
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if not q:
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return ok([])
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chunks = (
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db.query(KnowledgeChunk)
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.filter(KnowledgeChunk.avatar_id == avatar_id)
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.all()
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)
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if not chunks:
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return ok([])
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qvec = embeddings.embed([q])[0]
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scored = []
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for c in chunks:
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try:
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vec = json.loads(c.vector)
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except Exception:
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continue
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scored.append((embeddings.cosine(qvec, vec), c))
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scored.sort(key=lambda x: x[0], reverse=True)
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results = []
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for score, c in scored[: max(1, top_k)]:
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doc = db.query(KnowledgeDoc).filter(KnowledgeDoc.id == c.doc_id).first()
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snippet = c.content[:120] + ("…" if len(c.content) > 120 else "")
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results.append(
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{
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"docId": c.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": snippet,
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"score": round(score, 4),
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}
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)
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return ok(results)
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# ---------------- Standard Q&A pairs ----------------
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@router.get("/avatar/{avatar_id}/knowledge/qa")
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def list_qa(avatar_id: str, authorization: str = Header(None), db: Session = Depends(get_db)):
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_require_owned_avatar(db, avatar_id, authorization)
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items = (
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db.query(QAPair)
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.filter(QAPair.avatar_id == avatar_id)
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.order_by(QAPair.created_at.desc())
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.all()
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)
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return ok([q.to_dict() for q in items])
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@router.post("/avatar/{avatar_id}/knowledge/qa")
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def create_qa(avatar_id: str, body: QAIn, authorization: str = Header(None), db: Session = Depends(get_db)):
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_require_owned_avatar(db, avatar_id, authorization)
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q = QAPair(
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avatar_id=avatar_id,
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question=body.question,
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answer=body.answer,
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enabled=body.enabled,
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)
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db.add(q)
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db.commit()
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db.refresh(q)
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return ok(q.to_dict())
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@router.put("/avatar/{avatar_id}/knowledge/qa/{qa_id}")
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||||
def update_qa(avatar_id: str, qa_id: str, body: QAIn, authorization: str = Header(None), db: Session = Depends(get_db)):
|
||||
_require_owned_avatar(db, avatar_id, authorization)
|
||||
q = (
|
||||
db.query(QAPair)
|
||||
.filter(QAPair.id == qa_id, QAPair.avatar_id == avatar_id)
|
||||
.first()
|
||||
)
|
||||
if not q:
|
||||
return fail("问答对不存在", code=404)
|
||||
q.question = body.question
|
||||
q.answer = body.answer
|
||||
q.enabled = body.enabled
|
||||
db.commit()
|
||||
db.refresh(q)
|
||||
return ok(q.to_dict())
|
||||
|
||||
|
||||
@router.put("/avatar/{avatar_id}/knowledge/qa/{qa_id}/enabled")
|
||||
def set_qa_enabled(avatar_id: str, qa_id: str, body: EnabledIn, authorization: str = Header(None), db: Session = Depends(get_db)):
|
||||
_require_owned_avatar(db, avatar_id, authorization)
|
||||
q = (
|
||||
db.query(QAPair)
|
||||
.filter(QAPair.id == qa_id, QAPair.avatar_id == avatar_id)
|
||||
.first()
|
||||
)
|
||||
if not q:
|
||||
return fail("问答对不存在", code=404)
|
||||
q.enabled = bool(body.enabled)
|
||||
db.commit()
|
||||
db.refresh(q)
|
||||
return ok(q.to_dict())
|
||||
|
||||
|
||||
@router.delete("/avatar/{avatar_id}/knowledge/qa/{qa_id}")
|
||||
def delete_qa(avatar_id: str, qa_id: str, authorization: str = Header(None), db: Session = Depends(get_db)):
|
||||
_require_owned_avatar(db, avatar_id, authorization)
|
||||
q = (
|
||||
db.query(QAPair)
|
||||
.filter(QAPair.id == qa_id, QAPair.avatar_id == avatar_id)
|
||||
.first()
|
||||
)
|
||||
if not q:
|
||||
return fail("问答对不存在", code=404)
|
||||
db.delete(q)
|
||||
db.commit()
|
||||
return ok({"id": qa_id})
|
||||
|
||||
|
||||
# ---------------- HuiHui user profile (mock; plug real interface via HUIHUI_USER_API) ----------------
|
||||
@router.get("/user/profile")
|
||||
def user_profile():
|
||||
# 接入真实会会接口：设置环境变量 HUIHUI_USER_API 后在此请求并映射字段
|
||||
api = os.getenv("HUIHUI_USER_API")
|
||||
if api:
|
||||
# TODO: 调用会会用户接口，返回 { userId, nickname, avatarUrl }
|
||||
pass
|
||||
return ok({
|
||||
"userId": "hh_10001",
|
||||
"nickname": "会会用户",
|
||||
"avatarUrl": "https://api.dicebear.com/7.x/initials/svg?seed=HuiHui&backgroundColor=F97316",
|
||||
})
|
||||
Reference in New Issue
Block a user