多个agent tool结合

src/agent/rag_agent.py

@@ -15,6 +15,7 @@ from src.pgdb.knowledge.k_db import PostgresDB
 from src.pgdb.knowledge.txt_doc_table import TxtDoc
 from langchain_core.documents import Document
 import json
+from src.agent.tool_divisions import AdministrativeDivision
 from src.config.consts import (
     RERANK_MODEL_PATH,
     CHAT_DB_USER,
@@ -1555,12 +1556,12 @@ def jieba_split(text: str) -> str:
 
 class IssuanceArgs(BaseModel):
     question: str = Field(description="对话问题")
-    history: list = Field(description="历史对话记录")
+    history: str = Field(description="历史对话记录")
 
 
 class RAGQuery(BaseTool):
     name = "rag_query"
-    description = """Query the geological information of corresponding provinces, cities, and counties. Users can query geological information related to specific provinces, cities, and counties"""
+    description = """你是一个区（县）级的水文气象地质知识库，当咨询区（县）的水文气象地质等相关信息的时候，你可以提供数据和资料。注意，这个查询只能获取单个区（县）的水文气象地质等相关信息，当需要查询省市的详细信息时，需要获取改省市下的具体行政规划，并获取具体的区（县）的水文气象地质等相关信息"""
     args_schema: Type[BaseModel] = IssuanceArgs
     rerank: Any  # 替换 Any 为适当的类型
     rerank_model: Any  # 替换 Any 为适当的类型
@@ -1581,7 +1582,7 @@ class RAGQuery(BaseTool):
     def get_similarity_with_ext_origin(self, _ext):
         return GetSimilarityWithExt(_question=_ext, _faiss_db=self.faiss_db)
 
-    def _run(self, question: str, history: list) -> str:
+    def _run(self, question: str, history: str) -> str:
         split_str = jieba_split(question)
         split_list = []
         for l in split_str:
@@ -1593,18 +1594,15 @@ class RAGQuery(BaseTool):
             d = Document(page_content=a[0], metadata=json.loads(a[1]))
             split_docs.append(d)
         print(split_docs)
-        result = self.rerank.extend_query(question, history)
+        result = self.rerank.extend_query_with_str(question, history)
         matches = re.findall(r'"([^"]+)"', result.content)
         if len(matches) > 3:
             matches = matches[:3]
         print(matches)
-        prompt = ""
-        for h in history:
-            prompt += "问：{}\n答：{}\n\n".format(h[0], h[1])
         similarity = self.get_similarity_with_ext_origin(matches)
         # cur_similarity = similarity.get_rerank(self.rerank_model)
         cur_similarity = similarity.get_rerank_with_doc(self.rerank_model,split_docs)
-        cur_question = self.prompt.format(history=prompt, context=cur_similarity, question=question)
+        cur_question = self.prompt.format(history=history, context=cur_similarity, question=question)
         return cur_question
 
 
@@ -1631,7 +1629,7 @@ k_db.connect()
 
 
 
-tools = [RAGQuery(vecstore_faiss,ext,PromptTemplate(input_variables=["history","context", "question"], template=prompt_enhancement_history_template),_db=TxtDoc(k_db))]
+tools = [AdministrativeDivision(),RAGQuery(vecstore_faiss,ext,PromptTemplate(input_variables=["history","context", "question"], template=prompt_enhancement_history_template),_db=TxtDoc(k_db))]
 input_variables=['agent_scratchpad', 'input', 'tool_names', 'tools']
 input_types={'chat_history': List[Union[langchain_core.messages.ai.AIMessage, langchain_core.messages.human.HumanMessage, langchain_core.messages.chat.ChatMessage, langchain_core.messages.system.SystemMessage, langchain_core.messages.function.FunctionMessage, langchain_core.messages.tool.ToolMessage]]}
 metadata={'lc_hub_owner': 'hwchase17', 'lc_hub_repo': 'structured-chat-agent', 'lc_hub_commit_hash': 'ea510f70a5872eb0f41a4e3b7bb004d5711dc127adee08329c664c6c8be5f13c'}
@@ -1651,7 +1649,17 @@ prompt = ChatPromptTemplate(
 
 agent = create_structured_chat_agent(llm=base_llm, tools=tools, prompt=prompt)
 agent_executor = AgentExecutor(agent=agent, tools=tools,verbose=True,handle_parsing_errors=True)
-res = agent_executor.invoke({"input":"大通县"})
+history = []
+h1 = []
+h1.append("大通县年降雨量")
+h1.append("大通县年雨量平均为30ml")
+history.append(h1)
+
+prompt = ""
+for h in history:
+    prompt += "问：{}\n答：{}\n\n".format(h[0], h[1])
+print(prompt)
+res = agent_executor.invoke({"input":"以下历史对话记录： "+prompt+"以下是问题："+"西宁市年平均降雨量"})
 
 print("====== result: ======")
 print(res)

src/server/get_similarity.py

@@ -120,6 +120,20 @@ class QAExt:
             history += f"Q: {msg[0]}\nA: {msg[1]}\n"
         return self.query_extend.invoke(input={"histories":messages, "query":question})
 
+    def extend_query_with_str(self, question, messages):
+        """
+            question: str
+            messages: list of tuple (str,str)
+                eg:
+                [
+                    ("Q1","A1"),
+                    ("Q2","A2"),
+                    ...
+                ]
+
+        """
+        return self.query_extend.invoke(input={"histories": messages, "query": question})
+
 class ChatExtend:
     def __init__(self, llm) -> None:
         self.llm = llm