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.\nReference Docs\nAll of LangChain‚Äôs reference documentation, in one place. Full documentation on all methods, classes, installation methods, and integration setups for LangChain.\nReference Documentation\nLangChain Ecosystem\nGuides for how other companies/products can be used with LangChain\nLangChain Ecosystem\nAdditional Resources\nAdditional collection of resources we think may be useful as you develop your application!\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.\nTracing: A guide on using tracing in LangChain to visualize the execution of chains and agents.\nModel Laboratory: 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.\nDiscord: Join us on our Discord to discuss all things LangChain!\nProduction Support: As you move your LangChains into production, we‚Äôd love to offer more comprehensive support. Please fill out this form and we‚Äôll set up a dedicated support Slack channel.', metadata={'source': 'https://python.langchain.com/en/latest/index.html'})]PreviousDatadog LogsNextDiscordCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
Unlike traditional web scraping tools, Diffbot doesn't require any rules to read the content on a page.
Unlike traditional web scraping tools, Diffbot doesn't require any rules to read the content on a page. ->: 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.\nReference Docs\nAll of LangChain‚Äôs reference documentation, in one place. Full documentation on all methods, classes, installation methods, and integration setups for LangChain.\nReference Documentation\nLangChain Ecosystem\nGuides for how other companies/products can be used with LangChain\nLangChain Ecosystem\nAdditional Resources\nAdditional collection of resources we think may be useful as you develop your application!\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.\nTracing: A guide on using tracing in LangChain to visualize the execution of chains and agents.\nModel Laboratory: 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.\nDiscord: Join us on our Discord to discuss all things LangChain!\nProduction Support: As you move your LangChains into production, we‚Äôd love to offer more comprehensive support. Please fill out this form and we‚Äôll set up a dedicated support Slack channel.', metadata={'source': 'https://python.langchain.com/en/latest/index.html'})]PreviousDatadog LogsNextDiscordCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.