Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãOPromptsLanguage modelsLLMsChat modelsCachingHuman input chat modelLLMChainPromptsStreamingOutput parsersRetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesModel I/‚ÄãOLanguage modelsChat modelsHuman input chat modelHuman input chat modelAlong with HumanInputLLM, LangChain also provides a pseudo chat model class that can be used for testing, debugging, or educational purposes. This allows you to mock out calls to the chat model and simulate how a human would respond if they received the messages.In this notebook, we go over how to use this.We start this with using the HumanInputChatModel in an agent.from langchain.chat_models.human import HumanInputChatModelSince we will use the WikipediaQueryRun tool in this notebook, you might need to install the wikipedia package if you haven't done so already.%pip install wikipedia    /Users/mskim58/dev/research/chatbot/github/langchain/.venv/bin/python: No module named pip    Note: you may need to restart the kernel to use updated packages.from langchain.agents import load_toolsfrom langchain.agents import initialize_agentfrom langchain.agents import AgentTypetools = load_tools(["wikipedia"])llm = HumanInputChatModel()agent = initialize_agent(    tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent("What is Bocchi the Rock?")            > Entering new  chain...         ======= start of message =======             type: system    data:      content: "Answer the following questions as best you can. You have access to the following tools:\n\nWikipedia: A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, facts, historical events, or other
Along with HumanInputLLM, LangChain also provides a pseudo chat model class that can be used for testing, debugging, or educational purposes. This allows you to mock out calls to the chat model and simulate how a human would respond if they received the messages.
Along with HumanInputLLM, LangChain also provides a pseudo chat model class that can be used for testing, debugging, or educational purposes. This allows you to mock out calls to the chat model and simulate how a human would respond if they received the messages. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãOPromptsLanguage modelsLLMsChat modelsCachingHuman input chat modelLLMChainPromptsStreamingOutput parsersRetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesModel I/‚ÄãOLanguage modelsChat modelsHuman input chat modelHuman input chat modelAlong with HumanInputLLM, LangChain also provides a pseudo chat model class that can be used for testing, debugging, or educational purposes. This allows you to mock out calls to the chat model and simulate how a human would respond if they received the messages.In this notebook, we go over how to use this.We start this with using the HumanInputChatModel in an agent.from langchain.chat_models.human import HumanInputChatModelSince we will use the WikipediaQueryRun tool in this notebook, you might need to install the wikipedia package if you haven't done so already.%pip install wikipedia    /Users/mskim58/dev/research/chatbot/github/langchain/.venv/bin/python: No module named pip    Note: you may need to restart the kernel to use updated packages.from langchain.agents import load_toolsfrom langchain.agents import initialize_agentfrom langchain.agents import AgentTypetools = load_tools(["wikipedia"])llm = HumanInputChatModel()agent = initialize_agent(    tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent("What is Bocchi the Rock?")            > Entering new  chain...         ======= start of message =======             type: system    data:      content: "Answer the following questions as best you can. You have access to the following tools:\n\nWikipedia: A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, facts, historical events, or other