226 lines
11 KiB
Python
226 lines
11 KiB
Python
# -*- coding: utf-8 -*-
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"""
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对话管线（RAG 混合检索 + 函数调用）：
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1. 系统提示词 + 用户问题 → DeepSeek（带 8 个工具）
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2. 模型选择工具 → 执行（SQLite 结构化查询 + Chroma 向量检索）
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3. 工具结果回填 → DeepSeek 生成最终答案（数据准确，标注来源）
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4. 兜底：LLM 或工具异常时，用关键词预检索注入上下文后直接回答
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"""
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import json
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import logging
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import re
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import llm
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import tools as tools_mod
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from db import query
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log = logging.getLogger("chat")
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SYSTEM_PROMPT = """你是「NBA球迷大全」智能助手，为球迷提供比赛、球员、球队、新闻、人物等准确信息。
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工作准则：
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1. 用户问到时事、数据、赛程类问题，必须先调用工具查询数据库，用工具返回的真实数据回答，严禁编造具体比分、数据、日期。
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2. 工具结果就是权威数据源。回答时引用关键数据（比分、时间、数据），并标注来源（如「数据库」「新闻」）。
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3. 多步问题可以连续调用多个工具（如先查球队，再查该队比赛）。比较多个球员/球队时，把每个名字拆开单独调用一次工具（例如“约基奇和字母哥谁强”应分别调用 search_players("约基奇") 与 search_players("字母哥")）。
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4. 查不到时如实说"数据库暂未收录"，可以基于常识补充介绍，但要明确区分"数据库数据"与"常识补充"。
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5. 回答使用中文，简洁有条理，可适当使用小标题或列表；不要啰嗦。
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6. 严禁在回答正文中输出 tool_calls、XML 或函数调用代码——需要数据时直接调用工具函数，或直接如实回答；调用了工具就用工具返回的数据作答。
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7. 当前赛季为 2025-26 赛季，总决赛已于 2026年6月结束，雷霆 4-2 击败凯尔特人夺冠。"""
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def _tool_result_to_text(name, result):
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"""把工具结果压缩成给模型的文本（控制 token 量）"""
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if not result or result.get("_source") == "error":
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return f"[工具 {name}] 查询失败：{result.get('error','未知错误') if result else '无结果'}"
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results = result.get("results", [])
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if not results:
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return f"[工具 {name}] 未找到相关记录。"
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lines = [f"[工具 {name}] 查询到 {len(results)} 条记录："]
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for r in results[:8]:
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lines.append(json.dumps(r, ensure_ascii=False)[:600])
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return "\n".join(lines)
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def _grounding_context(user_msg):
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"""兜底检索：用关键词在数据库里快速找相关记录，注入上下文"""
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ctx = []
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for name, fn in (("teams", tools_mod.search_teams), ("players", tools_mod.search_players),
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("games", tools_mod.search_games), ("news", tools_mod.search_news),
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("persons", tools_mod.search_persons)):
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try:
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r = fn(user_msg, limit=3)
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if r.get("results"):
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ctx.append(_tool_result_to_text(name, r))
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except Exception:
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continue
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return "\n".join(ctx)
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def chat_once(user_msg, history=None):
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"""单轮对话。返回 (reply, sources, used_tools)
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sources: 供前端展示的信息来源卡片
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"""
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history = history or []
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messages = [{"role": "system", "content": SYSTEM_PROMPT}]
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for h in history[-10:]:
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messages.append({"role": "user", "content": h.get("user", "")})
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if h.get("assistant"):
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messages.append({"role": "assistant", "content": h["assistant"]})
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messages.append({"role": "user", "content": user_msg})
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used_tools, sources = [], []
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# ---- 第 1 轮：带工具
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try:
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resp = llm.chat(messages, tools=tools_mod.TOOLS)
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except Exception as e:
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log.warning("LLM 首轮失败(%s)，走兜底管线", e)
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ctx = _grounding_context(user_msg)
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msgs = messages + ([{"role": "system", "content": f"以下是数据库检索到的可能相关信息（未直接命中时勿强行引用）：\n{ctx}"}] if ctx else [])
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try:
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resp2 = llm.chat(msgs)
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return llm.parse_content(resp2), _mk_sources(ctx), [t for t in ("grounding",) if ctx]
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except Exception as e2:
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return (f"抱歉，大模型服务暂时不可用（{e2}）。你可以稍后再试，或直接浏览下方数据页面。", [], [])
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# ---- 工具执行（单轮）+ 实体覆盖补全 → 最终无工具作答
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all_executed = []
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calls = llm.extract_tool_calls(resp)
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if not calls:
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calls = _parse_text_tool_calls(llm.parse_content(resp), user_msg)
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if not calls:
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return llm.parse_content(resp) or "（模型未返回内容）", [], []
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executed = []
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for c in calls:
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if c["name"] in [e[0] for e in executed]:
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continue
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result = tools_mod.run_tool(c["name"], c["arguments"])
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log.info("[tools] %s(%s) -> %d条", c["name"],
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json.dumps(c["arguments"], ensure_ascii=False)[:120], len(result.get("results", [])))
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executed.append((c["name"], c["id"], c["arguments"], result))
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used_tools.append(c["name"])
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if result.get("results"):
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sources.append({"tool": c["name"], "items": result["results"][:3]})
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all_executed += executed
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# 覆盖补全：用户问题里提到的其他实体（多球员/多球队对比）自动补查，避免模型漏调
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covered = set()
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for _n, _cid, _args, result in executed:
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for r in result.get("results", []):
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covered.add(r.get("name") or r.get("team") or "")
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for term in tools_mod._split_terms(user_msg)[:4]:
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for fn, key in ((tools_mod.search_players, "name"), (tools_mod.search_teams, "name")):
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try:
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r = fn(term, limit=3)
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except Exception:
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continue
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for row in r.get("results", []):
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nm = row.get(key) or row.get("name") or ""
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if nm and nm not in covered:
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cid = f"cover_{key}_{len(all_executed)}"
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executed.append(("search_players" if key == "name" else "search_teams", cid, {"query": term}, r))
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covered.add(nm)
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used_tools.append("search_players" if key == "name" else "search_teams")
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if r.get("results"):
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sources.append({"tool": "search_players" if key == "name" else "search_teams", "items": r["results"][:3]})
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break
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messages.append({"role": "assistant", "content": None,
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"reasoning_content": llm.extract_reasoning(resp),
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"tool_calls": [
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{"id": cid, "type": "function",
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"function": {"name": name, "arguments": json.dumps(args, ensure_ascii=False)}}
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for name, cid, args, _ in executed]})
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for name, cid, _args, result in executed:
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messages.append({"role": "tool", "tool_call_id": cid, "content": _tool_result_to_text(name, result)})
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try:
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resp = llm.chat(messages) # 最终轮：不带工具，让模型基于数据作答
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except Exception as e:
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log.warning("LLM 最终轮失败(%s)，返回工具结果摘要", e)
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return _finalize(all_executed, user_msg, messages, sources, used_tools), sources, used_tools
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reply = llm.parse_content(resp) or ""
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if not reply or "<tool_calls>" in reply or "search_" in reply:
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# 模型又输出工具调用文本 → 注入上下文再答一次
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return _finalize(all_executed, user_msg, messages, sources, used_tools), sources, used_tools
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return reply, sources, used_tools
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def _finalize(executed, user_msg, messages, sources, used_tools):
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"""工具循环结束后：把检索结果注入上下文，让模型再总结一次（无工具），失败则给原始摘要"""
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if not executed:
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return "抱歉，没有检索到相关信息。请换个问法，或直接浏览下方数据页面。"
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ctx = "\n\n".join(_tool_result_to_text(name, result) for name, _cid, _args, result in executed)
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msgs = [{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": user_msg},
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{"role": "assistant", "content": f"我已经查询了数据库，检索结果如下：\n{ctx}\n\n请基于以上数据回答用户的问题（不要提及'工具'，直接给出答案；数据不足时如实说明）。"}]
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try:
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r = llm.chat(msgs)
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return llm.parse_content(r) or _results_summary(executed)
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except Exception:
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return _results_summary(executed)
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def _results_summary(executed):
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"""把已执行工具的结果整理成给用户的摘要文本"""
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parts = ["以下是数据库查到的相关信息："]
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for name, _cid, _args, result in executed:
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parts.append(_tool_result_to_text(name, result))
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return "\n".join(parts)
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def _parse_text_tool_calls(content, user_msg=""):
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"""解析模型以正文形式输出的工具调用（兜底），返回与 extract_tool_calls 同构的列表
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支持：真实工具名 / 模型臆造的工具名（get_game_stats、search_player_stats 等）→ 映射到最接近的真实工具"""
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if not content:
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return []
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names = re.findall(r"[a-z_]+_[a-z_]+", content)
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real = [n for n in names if n in tools_mod.TOOL_HANDLERS]
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game_id = None
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m = re.search(r"game_id[\"':=>]+\s*(\d+)", content)
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if m:
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game_id = int(m.group(1))
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out = []
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if real:
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for i, n in enumerate(dict.fromkeys(real)):
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q = re.findall(r"[\"'“”]([^\"'“”]{1,80})[\"'“”]", content)
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qv = q[i] if i < len(q) else user_msg
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args = {"query": qv} if n != "get_game_detail" else {"game_id": game_id or 0}
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out.append({"name": n, "arguments": args, "id": f"text_{i}"})
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return out
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# 模型臆造的工具名 → 智能映射
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game_intent = bool(re.search(r"总决赛|比赛|技术统计|统计|G\d|第.场|比分|对位", user_msg or ""))
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if game_id and any("game" in n or "stat" in n for n in names):
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out.append({"name": "get_game_detail", "arguments": {"game_id": game_id}, "id": "text_g"})
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elif game_intent and any("stat" in n or "score" in n or "game" in n for n in names):
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out.append({"name": "search_games", "arguments": {"query": user_msg}, "id": "text_g2"})
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elif any("player" in n or "stat" in n for n in names):
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out.append({"name": "search_players", "arguments": {"query": user_msg}, "id": "text_p"})
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elif any("game" in n or "match" in n for n in names):
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out.append({"name": "search_games", "arguments": {"query": user_msg}, "id": "text_g2"})
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elif any("news" in n for n in names):
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out.append({"name": "search_news", "arguments": {"query": user_msg}, "id": "text_n"})
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return out
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def _mk_sources(ctx):
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return [{"tool": "grounding", "items": [{"note": "关键词预检索上下文"}]}] if ctx else []
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def suggest_questions():
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"""快捷问题（前端展示用）"""
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return [
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"最近一场比赛结果",
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"湖人本赛季战绩怎么样",
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"库里本赛季场均数据",
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"2026年总决赛谁赢了",
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"SGA拿了什么荣誉",
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"NBA工资帽是什么",
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"介绍一下波波维奇",
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"今天有什么新闻",
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"西部排名",
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"雷霆和凯尔特人总决赛G6数据",
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]
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