Debugging chains | ü¶úÔ∏èüîó Langchain






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/‚ÄãORetrievalChainsHow toAsync APIDifferent call methodsCustom chainDebugging chainsLoading from LangChainHubAdding memory (state)Using OpenAI functionsSerializationFoundationalDocumentsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesChainsHow toDebugging chainsDebugging chainsIt can be hard to debug a Chain object solely from its output as most Chain objects involve a fair amount of input prompt preprocessing and LLM output post-processing.Setting verbose to True will print out some internal states of the Chain object while it is being ran.conversation = ConversationChain(    llm=chat,    memory=ConversationBufferMemory(),    verbose=True)conversation.run("What is ChatGPT?")    > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.    Current conversation:    Human: What is ChatGPT?    AI:    > Finished chain.    'ChatGPT is an AI language model developed by OpenAI. It is based on the GPT-3 architecture and is capable of generating human-like responses to text prompts. ChatGPT has been trained on a massive amount of text data and can understand and respond to a wide range of topics. It is often used for chatbots, virtual assistants, and other conversational AI applications.'PreviousCustom chainNextLoading from LangChainHubCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
It can be hard to debug a Chain object solely from its output as most Chain objects involve a fair amount of input prompt preprocessing and LLM output post-processing.
It can be hard to debug a Chain object solely from its output as most Chain objects involve a fair amount of input prompt preprocessing and LLM output post-processing. ->: Debugging chains | ü¶úÔ∏èüîó Langchain






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/‚ÄãORetrievalChainsHow toAsync APIDifferent call methodsCustom chainDebugging chainsLoading from LangChainHubAdding memory (state)Using OpenAI functionsSerializationFoundationalDocumentsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesChainsHow toDebugging chainsDebugging chainsIt can be hard to debug a Chain object solely from its output as most Chain objects involve a fair amount of input prompt preprocessing and LLM output post-processing.Setting verbose to True will print out some internal states of the Chain object while it is being ran.conversation = ConversationChain(    llm=chat,    memory=ConversationBufferMemory(),    verbose=True)conversation.run("What is ChatGPT?")    > Entering new ConversationChain chain...    Prompt after formatting:    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.    Current conversation:    Human: What is ChatGPT?    AI:    > Finished chain.    'ChatGPT is an AI language model developed by OpenAI. It is based on the GPT-3 architecture and is capable of generating human-like responses to text prompts. ChatGPT has been trained on a massive amount of text data and can understand and respond to a wide range of topics. It is often used for chatbots, virtual assistants, and other conversational AI applications.'PreviousCustom chainNextLoading from LangChainHubCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.