Method build_runner

bmtools/agent/tools_controller.py:74–114

(self, )

            self.tools_pool.append(tool)
        
    def build_runner(self, ):
        from langchain.vectorstores import FAISS
        from langchain.docstore import InMemoryDocstore
        from langchain.embeddings import OpenAIEmbeddings
        embeddings_model = OpenAIEmbeddings()
        import faiss
        embedding_size = 1536
        index = faiss.IndexFlatL2(embedding_size)
        vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})
        
        from .autogptmulti.agent import AutoGPT
        from langchain.chat_models import ChatOpenAI
        agent_executor = AutoGPT.from_llm_and_tools(
            ai_name="Tom",
            ai_role="Assistant",
            tools=self.tools_pool,
            llm=ChatOpenAI(temperature=0),
            memory=vectorstore.as_retriever()
        )
        '''
        # 可以修改prompt来让模型表现更好，也可以修改tool的doc
        prefix = """Answer the following questions as best you can. In this level, you are calling the tools in natural language format, since the tools are actually an intelligent agent like you, but they expert only in one area. Several things to remember. (1) Remember to follow the format of passing natural language as the Action Input. (2) DO NOT use your imagination, only use concrete information given by the tools. (3) If the observation contains images or urls which has useful information, YOU MUST INCLUDE ALL USEFUL IMAGES and links in your Answer and Final Answers using format ![img](url). BUT DO NOT provide any imaginary links. (4) The information in your Final Answer should include ALL the informations returned by the tools. (5) If a user's query is a language other than English, please translate it to English without tools, and translate it back to the source language in Final Answer. You have access to the following tools (Only use these tools we provide you):"""
        suffix = """\nBegin! Remember to . \nQuestion: {input}\n{agent_scratchpad}"""

    
        prompt = ZeroShotAgent.create_prompt(
            self.tools_pool,
            prefix=prefix,
            suffix=suffix, 
            input_variables=["input", "agent_scratchpad"]
        )
        llm_chain = LLMChain(llm=self.llm, prompt=prompt)
        logger.info("Full Prompt Template:\n {}".format(prompt.template))
        tool_names = [tool.name for tool in self.tools_pool]
        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)
        if self.stream_output:
            agent_executor = Executor.from_agent_and_tools(agent=agent, tools=self.tools_pool, verbose=True, return_intermediate_steps=True)
        else:
            agent_executor = AgentExecutorWithTranslation.from_agent_and_tools(agent=agent, tools=self.tools_pool, verbose=True, return_intermediate_steps=True)
        '''
        return agent_executor