Transformation | 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 toFoundationalLLMRouterSequentialTransformationDocumentsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesChainsFoundationalTransformationTransformationThis notebook showcases using a generic transformation chain.As an example, we will create a dummy transformation that takes in a super long text, filters the text to only the first 3 paragraphs, and then passes that into an LLMChain to summarize those.from langchain.chains import TransformChain, LLMChain, SimpleSequentialChainfrom langchain.llms import OpenAIfrom langchain.prompts import PromptTemplatewith open("../../state_of_the_union.txt") as f:    state_of_the_union = f.read()def transform_func(inputs: dict) -> dict:    text = inputs["text"]    shortened_text = "\n\n".join(text.split("\n\n")[:3])    return {"output_text": shortened_text}transform_chain = TransformChain(    input_variables=["text"], output_variables=["output_text"], transform=transform_func)template = """Summarize this text:{output_text}Summary:"""prompt = PromptTemplate(input_variables=["output_text"], template=template)llm_chain = LLMChain(llm=OpenAI(), prompt=prompt)sequential_chain = SimpleSequentialChain(chains=[transform_chain, llm_chain])sequential_chain.run(state_of_the_union)    ' The speaker addresses the nation, noting that while last year they were kept apart due to COVID-19, this year they are together again. They are reminded that regardless of their political affiliations, they are all Americans.'PreviousSequentialNextDocumentsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
This notebook showcases using a generic transformation chain.
This notebook showcases using a generic transformation chain. ->: Transformation | 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 toFoundationalLLMRouterSequentialTransformationDocumentsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesChainsFoundationalTransformationTransformationThis notebook showcases using a generic transformation chain.As an example, we will create a dummy transformation that takes in a super long text, filters the text to only the first 3 paragraphs, and then passes that into an LLMChain to summarize those.from langchain.chains import TransformChain, LLMChain, SimpleSequentialChainfrom langchain.llms import OpenAIfrom langchain.prompts import PromptTemplatewith open("../../state_of_the_union.txt") as f:    state_of_the_union = f.read()def transform_func(inputs: dict) -> dict:    text = inputs["text"]    shortened_text = "\n\n".join(text.split("\n\n")[:3])    return {"output_text": shortened_text}transform_chain = TransformChain(    input_variables=["text"], output_variables=["output_text"], transform=transform_func)template = """Summarize this text:{output_text}Summary:"""prompt = PromptTemplate(input_variables=["output_text"], template=template)llm_chain = LLMChain(llm=OpenAI(), prompt=prompt)sequential_chain = SimpleSequentialChain(chains=[transform_chain, llm_chain])sequential_chain.run(state_of_the_union)    ' The speaker addresses the nation, noting that while last year they were kept apart due to COVID-19, this year they are together again. They are reminded that regardless of their political affiliations, they are all Americans.'PreviousSequentialNextDocumentsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.