refactor(conv):重构对话管理器以使用 LCEL 链

- 更新了 LangChain 导入路径以使用 langchain_core
- 实现了带 RAG 的 LCEL 链用于对话处理
- 添加了构建对话历史消息的方法
- 实现了从知识库检索上下文的逻辑
- 移除了旧的对话历史构建方法- 更新了聊天方法以使用新的 LCEL 链- 修复了代码以适应新的导入结构

.claude/settings.local.json

@@ -11,7 +11,9 @@
       "Bash(git init:*)",
       "Bash(git remote add:*)",
       "Bash(git add:*)",
-      "Bash(rm:*)"
+      "Bash(rm:*)",
+      "Bash(git commit:*)",
+      "Bash(git push:*)"
     ],
     "deny": [],
     "ask": []

app/services/agent_orchestrator.py

@@ -5,7 +5,7 @@ from typing import List, Dict, Any, Optional
 from datetime import datetime
 from sqlalchemy.orm import Session
 from langchain.agents import AgentExecutor, create_openai_tools_agent
-from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 
 from app.db.models import ToolCall
 from app.services.model_manager import ModelManager

app/services/conv_manager.py

@@ -1,8 +1,12 @@
 """
 支持 RAG 的多轮对话管理器。
 """
-from typing import List, Dict, Any, Optional
+from typing import List, Dict, Any, Optional, Tuple
 from sqlalchemy.orm import Session
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough, RunnableLambda
+from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
 
 from app.db.models import Conversation, Message
 from app.services.model_manager import ModelManager
@@ -162,6 +166,107 @@ class ConversationManager:
 
         return messages
 
+    def _build_chat_history_messages(self, conversation_id: int, limit: int = 10) -> List:
+        """
+        构建对话历史消息列表（LangChain 消息格式）。
+
+        Args:
+            conversation_id: 对话 ID
+            limit: 要包含的最大消息数
+
+        Returns:
+            List: LangChain 消息对象列表
+        """
+        messages = self.get_messages(conversation_id, limit=limit)
+
+        history_messages = []
+        for msg in messages[:-1]:  # 排除最后一条用户消息（将单独添加）
+            if msg.role == "user":
+                history_messages.append(HumanMessage(content=msg.content))
+            elif msg.role == "assistant":
+                history_messages.append(AIMessage(content=msg.content))
+
+        return history_messages
+
+    def _retrieve_context(self, kb_id: int, query: str, k: int = 3) -> Tuple[str, List[Dict]]:
+        """
+        从知识库检索上下文。
+
+        Args:
+            kb_id: 知识库 ID
+            query: 查询文本
+            k: 返回的文档数量
+
+        Returns:
+            Tuple: (格式化的上下文字符串, 来源元数据列表)
+        """
+        try:
+            results = self.kb_manager.query_kb(kb_id, query, k=k)
+            context_parts = []
+            sources = []
+
+            for i, result in enumerate(results, 1):
+                content = result['content']
+                if len(content) > 500:
+                    content = f"{content[:500]}..."
+                context_parts.append(f"[{i}] {content}")
+                sources.append(result['metadata'])
+
+            context = "\n\n".join(context_parts)
+            logger.info("rag_context_retrieved", kb_id=kb_id, source_count=len(results))
+            return context, sources
+        except Exception as e:
+            logger.warning("rag_context_failed", error=str(e))
+            return "(No relevant context found)", []
+
+    def _create_rag_chain(self, llm, kb_id: Optional[int] = None):
+        """
+        创建带 RAG 的 LCEL 链。
+
+        Args:
+            llm: LLM 实例
+            kb_id: 可选的知识库 ID
+
+        Returns:
+            Runnable: LCEL 链
+        """
+        if kb_id:
+            # 带 RAG 的提示模板
+            prompt = ChatPromptTemplate.from_messages([
+                ("system", "You are a helpful AI assistant. Use the following context from the knowledge base to answer the question.\n\nContext:\n{context}"),
+                MessagesPlaceholder(variable_name="chat_history", optional=True),
+                ("human", "{input}"),
+            ])
+
+            # 创建检索器函数
+            def retrieve_context(inputs):
+                context, sources = self._retrieve_context(kb_id, inputs["input"])
+                return {
+                    **inputs,
+                    "context": context,
+                    "_sources": sources  # 保存来源以供后续使用
+                }
+
+            # LCEL 链: 检索 -> 提示 -> LLM -> 解析输出
+            chain = (
+                RunnableLambda(retrieve_context)
+                | RunnablePassthrough.assign(
+                    response=prompt | llm | StrOutputParser()
+                )
+            )
+        else:
+            # 无 RAG 的简单提示模板
+            prompt = ChatPromptTemplate.from_messages([
+                ("system", "You are a helpful AI assistant."),
+                MessagesPlaceholder(variable_name="chat_history", optional=True),
+                ("human", "{input}"),
+            ])
+
+            # LCEL 链: 提示 -> LLM -> 解析输出
+            chain = prompt | llm | StrOutputParser()
+
+        return chain
+
     async def chat(
         self,
         conversation_id: int,
@@ -171,7 +276,7 @@ class ConversationManager:
         use_rag: bool = True,
     ) -> Message:
         """
-        处理用户消息并生成助手回复。
+        处理用户消息并生成助手回复（使用 LCEL）。
 
         Args:
             conversation_id: 对话 ID
@@ -199,57 +304,39 @@ class ConversationManager:
         # 保存用户消息
         self.add_message(conversation_id, "user", user_input)
 
-        # 获取对话历史
-        history = self._build_history(conversation_id, limit=10)
-
         # 获取 LLM
         llm = self.model_manager.get_llm(llm_name)
 
-        # 如果启用了 RAG，从知识库构建上下文
+        # 获取对话历史（LangChain 消息格式）
-        context = ""
+        chat_history = self._build_chat_history_messages(conversation_id, limit=10)
-        sources = []
-        if use_rag and kb_id:
-            try:
-                results = self.kb_manager.query_kb(kb_id, user_input, k=3)
-                context_parts = []
-                for i, result in enumerate(results, 1):
-                    context_parts.append(
-                        f"[{i}] {result['content'][:500]}..."
-                        if len(result['content']) > 500
-                        else f"[{i}] {result['content']}"
-                    )
-                    sources.append(result['metadata'])
-                context = "\n\n".join(context_parts)
-                logger.info("rag_context_retrieved", kb_id=kb_id, source_count=len(results))
-            except Exception as e:
-                logger.warning("rag_context_failed", error=str(e))
-                context = "(No relevant context found)"
 
-        # 构建提示
+        # 创建 LCEL 链
-        if context:
+        kb_id_for_rag = kb_id if use_rag else None
-            prompt = f"""You are a helpful AI assistant. Use the following context from the knowledge base to answer the question.
+        chain = self._create_rag_chain(llm, kb_id_for_rag)
 
-Context:
+        # 准备输入
-{context}
+        chain_input = {
+            "input": user_input,
+            "chat_history": chat_history,
+        }
 
-Conversation history:
+        # 执行链
-{history}
-
-User: {user_input}
-Assistant:"""
-        else:
-            prompt = f"""{history}
-
-User: {user_input}
-Assistant:"""
-
-        # 生成回复
         try:
-            response = llm.predict(prompt)
+            if kb_id_for_rag:
+                # 带 RAG 的链返回字典
+                result = await chain.ainvoke(chain_input)
+                response = result.get("response", "")
+                sources = result.get("_sources", [])
+            else:
+                # 简单链直接返回字符串
+                response = await chain.ainvoke(chain_input)
+                sources = []
+
             logger.info("llm_response_generated", conversation_id=conversation_id)
         except Exception as e:
             logger.error("llm_generation_failed", error=str(e))
             response = "I apologize, but I encountered an error generating a response. Please try again."
+            sources = []
 
         # 保存助手消息
         metadata = {}
@@ -265,28 +352,6 @@ Assistant:"""
         logger.info("chat_complete", conversation_id=conversation_id)
         return assistant_message
 
-    def _build_history(self, conversation_id: int, limit: int = 10) -> str:
-        """
-        构建对话历史字符串。
-
-        Args:
-            conversation_id: 对话 ID
-            limit: 要包含的最大消息数
-
-        Returns:
-            str: 格式化的对话历史
-        """
-        messages = self.get_messages(conversation_id, limit=limit)
-
-        history_parts = []
-        for msg in messages[:-1]:  # 排除最后一条用户消息（已在提示中）
-            if msg.role == "user":
-                history_parts.append(f"User: {msg.content}")
-            elif msg.role == "assistant":
-                history_parts.append(f"Assistant: {msg.content}")
-
-        return "\n".join(history_parts) if history_parts else "(No previous messages)"
-
     def delete_conversation(self, conversation_id: int) -> bool:
         """
         删除对话及其所有消息。

app/services/kb_manager.py

@@ -3,7 +3,7 @@
 """
 from typing import List, Dict, Any, Optional
 from sqlalchemy.orm import Session
-from langchain.schema import Document as LangChainDocument
+from langchain_core.documents import Document as LangChainDocument
 
 from app.db.models import KnowledgeBase, Document
 from app.services.model_manager import ModelManager

app/services/model_manager.py

@@ -6,8 +6,8 @@ import re
 from typing import Optional, List, Dict, Any
 
 from sqlalchemy.orm import Session
-from langchain.llms.base import BaseLLM
+from langchain_core.language_models.llms import BaseLLM
-from langchain.embeddings.base import Embeddings
+from langchain_core.embeddings import Embeddings
 from langchain_openai import ChatOpenAI, OpenAIEmbeddings
 
 from app.db.models import Model

app/tools/calculator.py

@@ -3,7 +3,7 @@
 """
 from typing import Type
 from pydantic import BaseModel, Field
-from langchain.tools import BaseTool
+from langchain_core.tools import BaseTool
 
 from app.utils.logger import get_logger
 

app/tools/retriever.py

@@ -3,7 +3,7 @@
 """
 from typing import Optional, Type
 from pydantic import BaseModel, Field
-from langchain.tools import BaseTool
+from langchain_core.tools import BaseTool
 
 from app.services.kb_manager import KnowledgeBaseManager
 from app.utils.logger import get_logger

app/utils/faiss_helper.py

@@ -5,8 +5,8 @@ import os
 from typing import List, Optional
 
 from langchain_community.vectorstores import FAISS
-from langchain.schema import Document as LangChainDocument
+from langchain_core.documents import Document as LangChainDocument
-from langchain.embeddings.base import Embeddings
+from langchain_core.embeddings import Embeddings
 
 from app.utils.exceptions import VectorStoreError
 from app.utils.logger import get_logger

app/utils/text_splitter.py

@@ -3,8 +3,8 @@
 """
 from typing import List
 
-from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain.schema import Document as LangChainDocument
+from langchain_core.documents import Document as LangChainDocument
 
 
 class TextSplitter: