bing搜索agent

agent/custom_agent.py

+
+from langchain.agents import Tool
+from langchain.tools import BaseTool
+from langchain import PromptTemplate, LLMChain
+from agent.custom_search import DeepSearch
+from langchain.agents import BaseSingleActionAgent, AgentOutputParser, LLMSingleActionAgent, AgentExecutor
+from typing import List, Tuple, Any, Union, Optional, Type
+from langchain.schema import AgentAction, AgentFinish
+from langchain.prompts import StringPromptTemplate
+from langchain.callbacks.manager import CallbackManagerForToolRun
+from langchain.base_language import BaseLanguageModel
+import re
+
+agent_template = """
+你现在是一个{role}。这里是一些已知信息：
+{related_content}
+{background_infomation}
+{question_guide}：{input}
+
+{answer_format}
+"""
+
+class CustomPromptTemplate(StringPromptTemplate):
+    template: str
+    tools: List[Tool]
+
+    def format(self, **kwargs) -> str:
+        intermediate_steps = kwargs.pop("intermediate_steps")
+        # 没有互联网查询信息
+        if len(intermediate_steps) == 0:
+            background_infomation = "\n"
+            role = "傻瓜机器人"
+            question_guide = "我现在有一个问题"
+            answer_format = "如果你知道答案，请直接给出你的回答！如果你不知道答案，请你只回答\"DeepSearch('搜索词')\"，并将'搜索词'替换为你认为需要搜索的关键词，除此之外不要回答其他任何内容。\n\n下面请回答我上面提出的问题！"
+
+        # 返回了背景信息
+        else:
+            # 根据 intermediate_steps 中的 AgentAction 拼装 background_infomation
+            background_infomation = "\n\n你还有这些已知信息作为参考：\n\n"
+            action, observation = intermediate_steps[0]
+            background_infomation += f"{observation}\n"
+            role = "聪明的 AI 助手"
+            question_guide = "请根据这些已知信息回答我的问题"
+            answer_format = ""
+
+        kwargs["background_infomation"] = background_infomation
+        kwargs["role"] = role
+        kwargs["question_guide"] = question_guide
+        kwargs["answer_format"] = answer_format
+        return self.template.format(**kwargs)
+
+class CustomSearchTool(BaseTool):
+    name: str = "DeepSearch"
+    description: str = ""
+
+    def _run(self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None):
+        return DeepSearch.search(query = query)
+
+    async def _arun(self, query: str):
+        raise NotImplementedError("DeepSearch does not support async")
+
+class CustomAgent(BaseSingleActionAgent):
+    @property
+    def input_keys(self):
+        return ["input"]
+
+    def plan(self, intermedate_steps: List[Tuple[AgentAction, str]],
+            **kwargs: Any) -> Union[AgentAction, AgentFinish]:
+        return AgentAction(tool="DeepSearch", tool_input=kwargs["input"], log="")
+
+class CustomOutputParser(AgentOutputParser):
+    def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
+        # group1 = 调用函数名字
+        # group2 = 传入参数
+        match = re.match(r'^[\s\w]*(DeepSearch)\(([^\)]+)\)', llm_output, re.DOTALL)
+        print(match)
+        # 如果 llm 没有返回 DeepSearch() 则认为直接结束指令
+        if not match:
+            return AgentFinish(
+                return_values={"output": llm_output.strip()},
+                log=llm_output,
+            )
+        # 否则的话都认为需要调用 Tool
+        else:
+            action = match.group(1).strip()
+            action_input = match.group(2).strip()
+            return AgentAction(tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output)
+
+
+class DeepAgent:
+    tool_name: str = "DeepSearch"
+    agent_executor: any
+    tools: List[Tool]
+    llm_chain: any
+
+    def query(self, related_content: str = "", query: str = ""):
+        tool_name = self.tool_name
+        result = self.agent_executor.run(related_content=related_content, input=query ,tool_name=self.tool_name)
+        return result
+
+    def __init__(self, llm: BaseLanguageModel, **kwargs):
+        tools = [
+                    Tool.from_function(
+                        func=DeepSearch.search,
+                        name="DeepSearch",
+                        description=""
+                    )
+                ]
+        self.tools = tools
+        tool_names = [tool.name for tool in tools]
+        output_parser = CustomOutputParser()
+        prompt = CustomPromptTemplate(template=agent_template,
+                                      tools=tools,
+                                      input_variables=["related_content","tool_name", "input", "intermediate_steps"])
+
+        llm_chain = LLMChain(llm=llm, prompt=prompt)
+        self.llm_chain = llm_chain
+
+        agent = LLMSingleActionAgent(
+            llm_chain=llm_chain,
+            output_parser=output_parser,
+            stop=["\nObservation:"],
+            allowed_tools=tool_names
+        )
+
+        agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
+        self.agent_executor = agent_executor
+

agent/custom_search.py

+import requests
+
+RapidAPIKey = "90bbe925ebmsh1c015166fc5e12cp14c503jsn6cca55551ae4"
+
+class DeepSearch:
+    def search(query: str = ""):
+        query = query.strip()
+
+        if query == "":
+            return ""
+
+        if RapidAPIKey == "":
+            return "请配置你的 RapidAPIKey"
+
+        url = "https://bing-web-search1.p.rapidapi.com/search"
+
+        querystring = {"q": query,
+                "mkt":"zh-cn","textDecorations":"false","setLang":"CN","safeSearch":"Off","textFormat":"Raw"}
+
+        headers = {
+            "Accept": "application/json",
+            "X-BingApis-SDK": "true",
+            "X-RapidAPI-Key": RapidAPIKey,
+            "X-RapidAPI-Host": "bing-web-search1.p.rapidapi.com"
+        }
+
+        response = requests.get(url, headers=headers, params=querystring)
+
+        data_list = response.json()['value']
+
+        if len(data_list) == 0:
+            return ""
+        else:
+            result_arr = []
+            result_str = ""
+            count_index = 0
+            for i in range(6):
+                item = data_list[i]
+                title = item["name"]
+                description = item["description"]
+                item_str = f"{title}: {description}"
+                result_arr = result_arr + [item_str]
+
+            result_str = "\n".join(result_arr)
+            return result_str
+

configs/model_config.py

@@ -47,7 +47,7 @@ llm_model_dict = {
     "chatglm-6b": {
         "name": "chatglm-6b",
         "pretrained_model_name": "THUDM/chatglm-6b",
-        "local_model_path": None,
+        "local_model_path": "/media/checkpoint/chatglm-6b",
         "provides": "ChatGLM"
     },
 
@@ -66,7 +66,7 @@ llm_model_dict = {
     "vicuna-13b-hf": {
         "name": "vicuna-13b-hf",
         "pretrained_model_name": "vicuna-13b-hf",
-        "local_model_path": None,
+        "local_model_path": "/media/checkpoint/vicuna-13b-hf",
         "provides": "LLamaLLM"
     },