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| 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 <https://docs.langchain.com/docs/>`_. For the JavaScript documentation, see `here <https://js.langchain.com/docs/>`_. | |
| Getting Started | |
| ---------------- | |
| Checkout the below guide for a walkthrough of how to get started using LangChain to create an Language Model application. | |
| - `Getting Started Documentation <./getting_started/getting_started.html>`_ | |
| .. toctree:: | |
| :maxdepth: 1 | |
| :caption: Getting Started | |
| :name: getting_started | |
| :hidden: | |
| getting_started/getting_started.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 <https://github.com/hwchase17/langchain-hub>`_: 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 <https://discord.gg/6adMQxSpJS>`_: Join us on our Discord to discuss all things LangChain! | |
| - `YouTube <./youtube.html>`_: A collection of the LangChain tutorials and videos. | |
| - `Production Support <https://forms.gle/57d8AmXBYp8PP8tZA>`_: 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 <https://github.com/hwchase17/langchain-hub> | |
| ./glossary.md | |
| ./gallery.rst | |
| ./deployments.md | |
| ./tracing.md | |
| ./use_cases/model_laboratory.ipynb | |
| Discord <https://discord.gg/6adMQxSpJS> | |
| ./youtube.md | |
| Production Support <https://forms.gle/57d8AmXBYp8PP8tZA> | |