feat: Add base project structure with API and web components

api/app/services/agent_server.py

@@ -0,0 +1,130 @@
+
+
+from typing import Any, List
+
+from langchain_openai import ChatOpenAI
+from langgraph.checkpoint.memory import InMemorySaver
+from pydantic import BaseModel
+
+from langchain.agents import create_agent, AgentState
+from langchain.agents.middleware import before_model
+from langchain.tools import tool
+from langchain_core.messages import RemoveMessage
+from langgraph.graph.message import REMOVE_ALL_MESSAGES
+from langgraph.runtime import Runtime
+
+from app.services.api_resquests_server import send_message, model, retrieval
+
+
+class config(BaseModel):
+    template_str:str
+    params:dict
+    model_configs: List[dict] = []
+    history_memory:bool
+    knowledge_base:bool
+
+class RemoryInput(BaseModel):
+    question: str
+    end_user_id: str
+    search_switch:str
+
+class ChatRequest(BaseModel):
+    end_user_id: str
+    message: str
+    search_switch:str
+    kb_ids: List[str] = []
+    similarity_threshold:float
+    vector_similarity_weight:float
+    top_k:int
+    hybrid:bool
+    token:str
+
+class RetrievalInput(BaseModel):
+    message: str
+    kb_ids: List[str] = []
+    similarity_threshold: float
+    vector_similarity_weight: float
+    top_k: int
+    hybrid: bool
+    token: str
+
+async  def tool_Retrieval(req):
+    tool_result = retrieval_search.invoke({
+        "message":req.message, "kb_ids":req.kb_ids,
+        "similarity_threshold":req.similarity_threshold, "vector_similarity_weight":req.vector_similarity_weight,
+        "top_k":req.top_k, "hybrid":req.hybrid, "token":req.token
+    })
+    return tool_result
+async def tool_memory(req):
+    tool_result = remory_sk.invoke({
+        "question": req.message,
+        "end_user_id": req.end_user_id,
+        "search_switch": req.search_switch
+    })
+    return tool_result
+
+
+@before_model
+# ========== 消息剪枝中间件 ==========
+def trim_messages(state: AgentState, runtime: Runtime) -> dict[str, Any] | None:
+    """保留前1条 + 最近3~4条消息"""
+    messages = state["messages"]
+    if len(messages) <= 10:
+        return None
+    first_msg = messages[0]
+    recent_messages = messages[-10:] if len(messages) % 2 == 0 else messages[-11:]
+    new_messages = [first_msg] + recent_messages
+
+    return {
+        "messages": [
+            RemoveMessage(id=REMOVE_ALL_MESSAGES),
+            *new_messages
+        ]
+    }
+
+##-----------历史记忆------------
+@ tool(args_schema=RemoryInput)
+def remory_sk(question: str, end_user_id: str, search_switch: str):
+    """
+      条件调用工具：
+      - 仅当 question 是疑问句时调用 send_message
+      - 根据 end_user_id 和 search_switch 参数决定是否执行检索
+
+      Args:
+          question: 用户的提问内容
+          end_user_id: 用户唯一标识符
+          search_switch: 搜索开关，控制是否执行检索
+
+      Returns:
+          检索结果或空字符串
+      """
+    # 移除关于 configurable 的描述，避免混淆
+    if not end_user_id or end_user_id == '123':
+        print("警告: 无效的 user_id 参数")
+        return ''
+
+    if search_switch in ['on', 'off'] or not search_switch:
+        print("警告: 无效的 search_switch 参数")
+        return ''
+    return send_message(end_user_id, question, '[]', search_switch)
+
+#-------------检索------------
+
+
+@ tool(args_schema=RetrievalInput)
+def retrieval_search(message,kb_ids,similarity_threshold,vector_similarity_weight,top_k,hybrid,token):
+    '''检索'''
+    search=retrieval(message,kb_ids,similarity_threshold,vector_similarity_weight,top_k,hybrid,token)
+    return search
+async  def create_dynamic_agent(model_name: str,model_id:str,PROMPT:str,token:str):
+    """根据模型名动态创建代理"""
+    model_name, api_key, api_base=await model(model_id,token)
+    llm = ChatOpenAI(model=model_name, base_url=api_base, temperature=0.2,api_key=api_key)
+    memory = InMemorySaver()
+    return create_agent(
+        llm,
+        tools=[remory_sk,retrieval_search],
+        middleware=[trim_messages],
+        checkpointer=memory,
+        system_prompt=PROMPT
+    )