event: metadata
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event: debug
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event: debug
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event: debug
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event: messages/metadata
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event: debug
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event: debug|conduct_research:112ad8d7-bbc3-68b9-5d81-cead45ae71fb
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event: debug|conduct_research:112ad8d7-bbc3-68b9-5d81-cead45ae71fb
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event: debug|conduct_research:112ad8d7-bbc3-68b9-5d81-cead45ae71fb
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event: messages/metadata
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event: debug|conduct_research:112ad8d7-bbc3-68b9-5d81-cead45ae71fb
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event: debug|conduct_research:112ad8d7-bbc3-68b9-5d81-cead45ae71fb
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event: debug|conduct_research:112ad8d7-bbc3-68b9-5d81-cead45ae71fb
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This can include Python REPLs, embeddings, search engines, and more. LangChain provides a large collection of common utils to use in your application.\\\\nChains: Chains go beyond just a single LLM call, and are sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.\\\\nIndexes: Language models are often more powerful when combined with your own text data - this module covers best practices for doing exactly that.\\\\nAgents: Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents.\\\\nMemory: Memory is the concept of persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.\\\\nChat: Chat models are a variation on Language Models that expose a different API - rather than working with raw text, they work with messages. LangChain provides a standard interface for working with them and doing all the same things as above.\\\\n\\\\n\\\\n\\\\n\\\\n\\\\nUse Cases#\\\\nThe above modules can be used in a variety of ways. LangChain also provides guidance and assistance in this. Below are some of the common use cases LangChain supports.\\\\n\\\\nAgents: Agents are systems that use a language model to interact with other tools. These can be used to do more grounded question/answering, interact with APIs, or even take actions.\\\\nChatbots: Since language models are good at producing text, that makes them ideal for creating chatbots.\\\\nData Augmented Generation: Data Augmented Generation involves specific types of chains that first interact with an external datasource to fetch data to use in the generation step. Examples of this include summarization of long pieces of text and question/answering over specific data sources.\\\\nQuestion Answering: Answering questions over specific documents, only utilizing the information in those documents to construct an answer. A type of Data Augmented Generation.\\\\nSummarization: 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 LangChain’s reference documentation, in one place. 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