callbacks抽象AgentExecutor过程中的一些交互事件,通过事件展示信息 chat_modelszhipuai sdk的封装层,提供langchain的BaseChatModel集成,格式化输入输出为消息体 embeddingszhipuai sdk的封装层,提供langchain的Embeddings集成 utils一些会话工具 快速使用 Python版本支持 ...
='exit':response=agent.invoke({"input":user_input})print(response)outputofsimpleexamplesare---Ask>whatissquareofnumber6>EnteringnewAgentExecutorchain...Invoking:`getSquareOfNumber`with`{'number': '6'}`InsidemethodgetSquareOfNumberThesquareof6is36Thesquareofthenumber6is36.>Finishedchain. {'input...
optimize_workflow_and_output xuyang1/finetune_prompts test_ci xuyang1/add_idea_task xuyang1/support_multi_task fix_pip_install_CI you-n-g-patch-7 6cma dependabot/pip/scripts/data_collector/br_index/requests-2.31.0 huoran/resolve_rl_fixmes ...
CITA 将区块链节点的必要功能解耦为六个微服务：RPC，Auth，Consensus，Chain，Executor，Network。
接下来使用 LangChain 对工具进行调用from langchain.agents import initialize_agent from langchain.llms import OpenAI from tools import WeatherTool from tools import CustomCalculatorTool llm = OpenAI(openai_api_key="xxx", temperature=0) tools = [WeatherTool(), CustomCalculatorTool()] agent = ...
'Unrecognized request argument supplied: functions' error when executing agent | following documentation Agent Executor Error code: 404 DOC: <Please write a comprehensive title after the 'DOC: ' prefix> Anyone successfully created an Agent with AzureChatOpenAI? libs/community/langchain_community/chat_...
由于使用传统的langchain的AgentExecutor 构建agent没有的灵活性和控制力,langchain官方已经推荐使用langGraph来创建根据灵活易用的langGraph来创建agent,并编写了从langchian的agent迁移到langGraph的教程,可见日后使用langGraph构建agent将会作为langchain团队的重心工作之一。因此本项目将特地翻译LangGraph的文档。 概述 Lang...
fromLLM(model, vectorStore); const kbTool = new ChainTool({ name: "Knowledge Base", description: "use this tool when answering general knowledge queries to get more information about the topic", chain, }); Finally, we'll create the agent executor that'll combine the model and the vector...
site-packages/langchain/agents/agent.py:905](https://vscode-remote+ssh-002dremote-002bknockdhu-002econcentricai-002ecom.vscode-resource.vscode-cdn.net/opt/anaconda3/envs/langchain/lib/python3.8/site-packages/langchain/agents/agent.py:905), in AgentExecutor._call(self, inputs, run_manager)...
python_agent = create_python_agent( llm=llm, tool=PythonREPLTool(), verbose=True, agent_type=AgentType.OPENAI_FUNCTIONS, agent_executor_kwargs={"handle_parsing_errors": True}, ) # type: ignore search = SerpAPIWrapper() # type: ignore llm_math_chain = LLMMathChain.from_llm(llm=llm, ...