相关agent编写

src/agent/rag_agent.py

+from langchain_core.tools import tool,BaseTool
+from pydantic import BaseModel, Field
+from typing import Type, Any,List,Union
+import re
+from src.server.get_similarity import GetSimilarityWithExt
+from langchain_openai import ChatOpenAI
+from langchain.agents import initialize_agent,Tool,AgentType,AgentExecutor,create_structured_chat_agent,create_react_agent,create_openai_functions_agent
+from langchain_core.prompts.chat import ChatPromptTemplate,HumanMessagePromptTemplate,SystemMessagePromptTemplate,MessagesPlaceholder
+from langchain_core.prompts import PromptTemplate
+import langchain_core
+from src.pgdb.knowledge.similarity import VectorStore_FAISS
+from src.server.get_similarity import QAExt
+from src.server.rerank import BgeRerank
+from src.config.consts import (
+    RERANK_MODEL_PATH,
+    CHAT_DB_USER,
+    CHAT_DB_HOST,
+    CHAT_DB_PORT,
+    CHAT_DB_DBNAME,
+    CHAT_DB_PASSWORD,
+    EMBEEDING_MODEL_PATH,
+    FAISS_STORE_PATH,
+    INDEX_NAME,
+    VEC_DB_HOST,
+    VEC_DB_PASSWORD,
+    VEC_DB_PORT,
+    VEC_DB_USER,
+    VEC_DB_DBNAME,
+    SIMILARITY_SHOW_NUMBER,
+    prompt_enhancement_history_template
+)
+
+
+
+class IssuanceArgs(BaseModel):
+    question: str = Field(description="对话问题")
+    history: list = 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"""
+    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
+    faiss_db: Any  # 替换 Any 为适当的类型
+    prompt: Any  # 假设 prompt 是一个字符串
+
+    def __init__(self,_faiss_db,_rerank,_prompt):
+        super().__init__()
+
+        self.rerank = _rerank
+        self.rerank_model = BgeRerank(RERANK_MODEL_PATH)
+        self.faiss_db = _faiss_db
+        self.prompt = _prompt
+
+
+    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:
+        result = self.rerank.extend_query(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_question = self.prompt.format(history=prompt, context=cur_similarity, question=question)
+        return cur_question
+
+
+base_llm = ChatOpenAI(
+    openai_api_key='xxxxxxxxxxxxx',
+    openai_api_base='http://192.168.10.14:8000/v1',
+    model_name='Qwen2-7B',
+    verbose=True,
+    temperature=0
+)
+
+vecstore_faiss = VectorStore_FAISS(
+        embedding_model_name=EMBEEDING_MODEL_PATH,
+        store_path=FAISS_STORE_PATH,
+        index_name=INDEX_NAME,
+        info={"port": VEC_DB_PORT, "host": VEC_DB_HOST, "dbname": VEC_DB_DBNAME, "username": VEC_DB_USER,
+              "password": VEC_DB_PASSWORD},
+        show_number=SIMILARITY_SHOW_NUMBER,
+        reset=False)
+ext = QAExt(base_llm)
+
+
+tools = [RAGQuery(vecstore_faiss,ext,PromptTemplate(input_variables=["history","context", "question"], template=prompt_enhancement_history_template))]
+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'}
+messages=[
+  SystemMessagePromptTemplate(prompt=PromptTemplate(input_variables=['tool_names', 'tools'], template='Respond to the human as helpfully and accurately as possible. You have access to the following tools:\n\n{tools}\n\nUse a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).\n\nValid "action" values: "Final Answer" or {tool_names}\n\nProvide only ONE action per $JSON_BLOB, as shown:\n\n```\n{{\n  "action": $TOOL_NAME,\n  "action_input": $INPUT\n}}\n```\n\nFollow this format:\n\nQuestion: input question to answer\nThought: consider previous and subsequent steps\nAction:\n```\n$JSON_BLOB\n```\nObservation: action result\n... (repeat Thought/Action/Observation N times)\nThought: I know what to respond\nAction:\n```\n{{\n  "action": "Final Answer",\n  "action_input": "Final response to human"\n}}\n\nBegin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation')),
+  MessagesPlaceholder(variable_name='chat_history', optional=True),
+  HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['agent_scratchpad', 'input'], template='{input}\n\n{agent_scratchpad}\n (reminder to respond in a JSON blob no matter what)'))
+]
+
+prompt = ChatPromptTemplate(
+    input_variables=input_variables,
+    input_types=input_types,
+    metadata=metadata,
+    messages=messages
+)
+
+
+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":"西宁市大通县水文情况"})
+
+print("====== result: ======")
+print(res)

src/agent/sql_agent.py

+from langchain_community.agent_toolkits import SQLDatabaseToolkit
+from langchain_openai import ChatOpenAI
+from langchain_community.utilities import SQLDatabase
+from langchain.agents import initialize_agent,Tool,AgentType,AgentExecutor,create_structured_chat_agent,create_react_agent,create_openai_functions_agent
+from langchain_core.prompts.chat import ChatPromptTemplate,HumanMessagePromptTemplate,SystemMessagePromptTemplate,MessagesPlaceholder
+from langchain_core.prompts import PromptTemplate
+import langchain_core
+from typing import Type, Any,List,Union
+
+
+base_llm = ChatOpenAI(
+    openai_api_key='xxxxxxxxxxxxx',
+    openai_api_base='http://192.168.10.14:8000/v1',
+    model_name='Qwen2-7B',
+    verbose=True,
+    temperature=0
+)
+
+db = SQLDatabase.from_uri("postgresql://postgres:111111@192.168.10.189:5433/lae")
+toolkit = SQLDatabaseToolkit(llm=base_llm, db=db)
+tools = toolkit.get_tools()
+print(tools)
+
+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'}
+messages=[
+  SystemMessagePromptTemplate(prompt=PromptTemplate(input_variables=['tool_names', 'tools'], template='Respond to the human as helpfully and accurately as possible. You have access to the following tools:\n\n{tools}\n\nUse a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).\n\nValid "action" values: "Final Answer" or {tool_names}\n\nProvide only ONE action per $JSON_BLOB, as shown:\n\n```\n{{\n  "action": $TOOL_NAME,\n  "action_input": $INPUT\n}}\n```\n\nFollow this format:\n\nQuestion: input question to answer\nThought: consider previous and subsequent steps\nAction:\n```\n$JSON_BLOB\n```\nObservation: action result\n... (repeat Thought/Action/Observation N times)\nThought: I know what to respond\nAction:\n```\n{{\n  "action": "Final Answer",\n  "action_input": "Final response to human"\n}}\n\nBegin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation')),
+  MessagesPlaceholder(variable_name='chat_history', optional=True),
+  HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['agent_scratchpad', 'input'], template='{input}\n\n{agent_scratchpad}\n (reminder to respond in a JSON blob no matter what)'))
+]
+
+prompt = ChatPromptTemplate(
+    input_variables=input_variables,
+    input_types=input_types,
+    metadata=metadata,
+    messages=messages
+)
+
+print(prompt)
+
+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":"武汉有什么好玩的景点"})
+
+print("====== result: ======")
+print(res)