text: Summarizing longer documents into shorter, more condensed chunks of information. A type of Data Augmented Generation.\nQuerying Tabular Data: If you want to understand how to use LLMs to query data that is stored in a tabular format (csvs, SQL, dataframes, etc) you should read this page.\nEvaluation: Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.\nGenerate similar examples: Generating similar examples to a given input. This is a common use case for many applications, and LangChain provides some prompts/chains for assisting in this.\nCompare models: Experimenting with different prompts, models, and chains is a big part of developing the best possible application. The ModelLaboratory makes it easy to do so.\n\n\n\n\n\nReference Docs#\nAll of LangChainâ€™s reference documentation, in one place. Full documentation on all methods, classes, installation methods, and integration setups for LangChain.\n\nReference Documentation\n\n\n\n\n\nLangChain Ecosystem#\nGuides for how other companies/products can be used with LangChain\n\nLangChain Ecosystem\n\n\n\n\n\nAdditional Resources#\nAdditional collection of resources we think may be useful as you develop your application!\n\nLangChainHub: The LangChainHub is a place to share and explore other prompts, chains, and agents.\nGlossary: A glossary of all related terms, papers, methods, etc. Whether implemented in LangChain or not!\nGallery: A collection of our favorite projects that use LangChain. Useful for finding inspiration or seeing how things were done in other applications.\nDeployments: A collection of instructions, code snippets, and template repositories for deploying LangChain apps.\nDiscord: Join us on our Discord to discuss all things