Welcome to LangChain ========================== LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call out to a language model via an API, but will also: - *Be data-aware*: connect a language model to other sources of data - *Be agentic*: allow a language model to interact with its environment The LangChain framework is designed with the above principles in mind. This is the Python specific portion of the documentation. For a purely conceptual guide to LangChain, see `here `_. For the JavaScript documentation, see `here `_. Getting Started ---------------- How to get started using LangChain to create an Language Model application. - `Getting Started tutorial <./getting_started/getting_started.html>`_ Tutorials created by community experts and presented on YouTube. - `Tutorials <./getting_started/tutorials.html>`_ .. toctree:: :maxdepth: 1 :caption: Getting Started :name: getting_started :hidden: getting_started/getting_started.md getting_started/tutorials.md Modules ----------- There are several main modules that LangChain provides support for. For each module we provide some examples to get started, how-to guides, reference docs, and conceptual guides. These modules are, in increasing order of complexity: - `Models <./modules/models.html>`_: The various model types and model integrations LangChain supports. - `Prompts <./modules/prompts.html>`_: This includes prompt management, prompt optimization, and prompt serialization. - `Memory <./modules/memory.html>`_: 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. - `Indexes <./modules/indexes.html>`_: Language models are often more powerful when combined with your own text data - this module covers best practices for doing exactly that. - `Chains <./modules/chains.html>`_: 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. - `Agents <./modules/agents.html>`_: 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. - `Callbacks <./modules/callbacks/getting_started.html>`_: It can be difficult to track all that occurs inside a chain or agent - callbacks help add a level of observability and introspection. .. toctree:: :maxdepth: 1 :caption: Modules :name: modules :hidden: ./modules/models.rst ./modules/prompts.rst ./modules/indexes.md ./modules/memory.md ./modules/chains.md ./modules/agents.md ./modules/callbacks/getting_started.ipynb Use Cases ---------- The 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. - `Autonomous Agents <./use_cases/autonomous_agents.html>`_: Autonomous agents are long running agents that take many steps in an attempt to accomplish an objective. Examples include AutoGPT and BabyAGI. - `Agent Simulations <./use_cases/agent_simulations.html>`_: Putting agents in a sandbox and observing how they interact with each other or to events can be an interesting way to observe their long-term memory abilities. - `Personal Assistants <./use_cases/personal_assistants.html>`_: The main LangChain use case. Personal assistants need to take actions, remember interactions, and have knowledge about your data. - `Question Answering <./use_cases/question_answering.html>`_: The second big LangChain use case. Answering questions over specific documents, only utilizing the information in those documents to construct an answer. - `Chatbots <./use_cases/chatbots.html>`_: Since language models are good at producing text, that makes them ideal for creating chatbots. - `Querying Tabular Data <./use_cases/tabular.html>`_: 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. - `Code Understanding <./use_cases/code.html>`_: If you want to understand how to use LLMs to query source code from github, you should read this page. - `Interacting with APIs <./use_cases/apis.html>`_: Enabling LLMs to interact with APIs is extremely powerful in order to give them more up-to-date information and allow them to take actions. - `Extraction <./use_cases/extraction.html>`_: Extract structured information from text. - `Summarization <./use_cases/summarization.html>`_: Summarizing longer documents into shorter, more condensed chunks of information. A type of Data Augmented Generation. - `Evaluation <./use_cases/evaluation.html>`_: 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. .. toctree:: :maxdepth: 1 :caption: Use Cases :name: use_cases :hidden: ./use_cases/personal_assistants.md ./use_cases/autonomous_agents.md ./use_cases/agent_simulations.md ./use_cases/question_answering.md ./use_cases/chatbots.md ./use_cases/tabular.rst ./use_cases/code.md ./use_cases/apis.md ./use_cases/summarization.md ./use_cases/extraction.md ./use_cases/evaluation.rst Reference Docs --------------- All of LangChain's reference documentation, in one place. Full documentation on all methods, classes, installation methods, and integration setups for LangChain. - `Reference Documentation <./reference.html>`_ .. toctree:: :maxdepth: 1 :caption: Reference :name: reference :hidden: ./reference/installation.md ./reference/integrations.md ./reference.rst LangChain Ecosystem ------------------- Guides for how other companies/products can be used with LangChain - `LangChain Ecosystem <./ecosystem.html>`_ .. toctree:: :maxdepth: 1 :glob: :caption: Ecosystem :name: ecosystem :hidden: ./ecosystem.rst Additional Resources --------------------- Additional collection of resources we think may be useful as you develop your application! - `LangChainHub `_: The LangChainHub is a place to share and explore other prompts, chains, and agents. - `Glossary <./glossary.html>`_: A glossary of all related terms, papers, methods, etc. Whether implemented in LangChain or not! - `Gallery <./gallery.html>`_: A collection of our favorite projects that use LangChain. Useful for finding inspiration or seeing how things were done in other applications. - `Deployments <./deployments.html>`_: A collection of instructions, code snippets, and template repositories for deploying LangChain apps. - `Tracing <./tracing.html>`_: A guide on using tracing in LangChain to visualize the execution of chains and agents. - `Model Laboratory <./model_laboratory.html>`_: 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. - `Discord `_: Join us on our Discord to discuss all things LangChain! - `YouTube <./youtube.html>`_: A collection of the LangChain tutorials and videos. - `Production Support `_: As you move your LangChains into production, we'd love to offer more comprehensive support. Please fill out this form and we'll set up a dedicated support Slack channel. .. toctree:: :maxdepth: 1 :caption: Additional Resources :name: resources :hidden: LangChainHub ./glossary.md ./gallery.rst ./deployments.md ./tracing.md ./use_cases/model_laboratory.ipynb Discord ./youtube.md Production Support