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 modelsLLMsAsync APICustom LLMFake LLMHuman input LLMCachingSerializationStreamingTracking token usageChat modelsOutput parsersRetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesModel I/‚ÄãOLanguage modelsLLMsHuman input LLMHuman input LLMSimilar to the fake LLM, LangChain provides a pseudo LLM class that can be used for testing, debugging, or educational purposes. This allows you to mock out calls to the LLM and simulate how a human would respond if they received the prompts.In this notebook, we go over how to use this.We start this with using the HumanInputLLM in an agent.from langchain.llms.human import HumanInputLLMfrom langchain.agents import load_toolsfrom langchain.agents import initialize_agentfrom langchain.agents import AgentTypeSince 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 wikipediatools = load_tools(["wikipedia"])llm = HumanInputLLM(    prompt_func=lambda prompt: print(        f"\n===PROMPT====\n{prompt}\n=====END OF PROMPT======"    ))agent = initialize_agent(    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run("What is 'Bocchi the Rock!'?")            > Entering new AgentExecutor chain...        ===PROMPT====    Answer the following questions as best you can. You have access to the following tools:        Wikipedia: A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, historical events, or other subjects. Input should be a search query.        Use the following format:        Question: the input question you must answer    Thought: you should
Similar to the fake LLM, LangChain provides a pseudo LLM class that can be used for testing, debugging, or educational purposes. This allows you to mock out calls to the LLM and simulate how a human would respond if they received the prompts.
Similar to the fake LLM, LangChain provides a pseudo LLM class that can be used for testing, debugging, or educational purposes. This allows you to mock out calls to the LLM and simulate how a human would respond if they received the prompts. ->: 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 modelsLLMsAsync APICustom LLMFake LLMHuman input LLMCachingSerializationStreamingTracking token usageChat modelsOutput parsersRetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesModel I/‚ÄãOLanguage modelsLLMsHuman input LLMHuman input LLMSimilar to the fake LLM, LangChain provides a pseudo LLM class that can be used for testing, debugging, or educational purposes. This allows you to mock out calls to the LLM and simulate how a human would respond if they received the prompts.In this notebook, we go over how to use this.We start this with using the HumanInputLLM in an agent.from langchain.llms.human import HumanInputLLMfrom langchain.agents import load_toolsfrom langchain.agents import initialize_agentfrom langchain.agents import AgentTypeSince 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 wikipediatools = load_tools(["wikipedia"])llm = HumanInputLLM(    prompt_func=lambda prompt: print(        f"\n===PROMPT====\n{prompt}\n=====END OF PROMPT======"    ))agent = initialize_agent(    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run("What is 'Bocchi the Rock!'?")            > Entering new AgentExecutor chain...        ===PROMPT====    Answer the following questions as best you can. You have access to the following tools:        Wikipedia: A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, historical events, or other subjects. Input should be a search query.        Use the following format:        Question: the input question you must answer    Thought: you should