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193 lines
7.9 KiB
ReStructuredText
Welcome to LangChain
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==========================
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| **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, but will also be:
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1. *Data-aware*: connect a language model to other sources of data
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2. *Agentic*: allow a language model to interact with its environment
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| The LangChain framework is designed around these principles.
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| 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/>`_.
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Getting Started
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----------------
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| How to get started using LangChain to create an Language Model application.
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- `Quickstart Guide <./getting_started/getting_started.html>`_
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| Concepts and terminology.
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- `Concepts and terminology <./getting_started/concepts.html>`_
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| Tutorials created by community experts and presented on YouTube.
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- `Tutorials <./getting_started/tutorials.html>`_
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.. toctree::
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:maxdepth: 2
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:caption: Getting Started
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:name: getting_started
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:hidden:
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getting_started/getting_started.md
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getting_started/concepts.md
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getting_started/tutorials.md
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Modules
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-----------
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| These modules are the core abstractions which we view as the building blocks of any LLM-powered application.
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For each module LangChain provides standard, extendable interfaces. LangChain also provides external integrations and even end-to-end implementations for off-the-shelf use.
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| The docs for each module contain quickstart examples, how-to guides, reference docs, and conceptual guides.
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| The modules are (from least to most complex):
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- `Models <./modules/models.html>`_: Supported model types and integrations.
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- `Prompts <./modules/prompts.html>`_: Prompt management, optimization, and serialization.
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- `Memory <./modules/memory.html>`_: Memory refers to state that is persisted between calls of a chain/agent.
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- `Indexes <./modules/indexes.html>`_: Language models become much more powerful when combined with application-specific data - this module contains interfaces and integrations for loading, querying and updating external data.
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- `Chains <./modules/chains.html>`_: Chains are structured sequences of calls (to an LLM or to a different utility).
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- `Agents <./modules/agents.html>`_: An agent is a Chain in which an LLM, given a high-level directive and a set of tools, repeatedly decides an action, executes the action and observes the outcome until the high-level directive is complete.
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- `Callbacks <./modules/callbacks/getting_started.html>`_: Callbacks let you log and stream the intermediate steps of any chain, making it easy to observe, debug, and evaluate the internals of an application.
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.. toctree::
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:maxdepth: 1
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:caption: Modules
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:name: modules
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:hidden:
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./modules/models.rst
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./modules/prompts.rst
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./modules/indexes.md
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./modules/memory.md
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./modules/chains.md
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./modules/agents.md
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./modules/callbacks/getting_started.ipynb
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Use Cases
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----------
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| Best practices and built-in implementations for common LangChain use cases:
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- `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.
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- `Agent Simulations <./use_cases/agent_simulations.html>`_: Putting agents in a sandbox and observing how they interact with each other and react to events can be an effective way to evaluate their long-range reasoning and planning abilities.
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- `Personal Assistants <./use_cases/personal_assistants.html>`_: One of the primary LangChain use cases. Personal assistants need to take actions, remember interactions, and have knowledge about your data.
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- `Question Answering <./use_cases/question_answering.html>`_: Another common LangChain use case. Answering questions over specific documents, only utilizing the information in those documents to construct an answer.
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- `Chatbots <./use_cases/chatbots.html>`_: Language models love to chat, making this a very natural use of them.
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- `Querying Tabular Data <./use_cases/tabular.html>`_: Recommended reading if you want to use language models to query structured data (CSVs, SQL, dataframes, etc).
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- `Code Understanding <./use_cases/code.html>`_: Recommended reading if you want to use language models to analyze code.
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- `Interacting with APIs <./use_cases/apis.html>`_: Enabling language models to interact with APIs is extremely powerful. It gives them access to up-to-date information and allows them to take actions.
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- `Extraction <./use_cases/extraction.html>`_: Extract structured information from text.
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- `Summarization <./use_cases/summarization.html>`_: Compressing longer documents. A type of Data-Augmented Generation.
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- `Evaluation <./use_cases/evaluation.html>`_: Generative models are hard to evaluate with traditional metrics. One promising approach is to use language models themselves to do the evaluation.
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.. toctree::
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:maxdepth: 1
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:caption: Use Cases
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:name: use_cases
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:hidden:
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./use_cases/autonomous_agents.md
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./use_cases/agent_simulations.md
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./use_cases/personal_assistants.md
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./use_cases/question_answering.md
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./use_cases/chatbots.md
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./use_cases/tabular.rst
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./use_cases/code.md
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./use_cases/apis.md
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./use_cases/summarization.md
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./use_cases/extraction.md
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./use_cases/evaluation.rst
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Reference Docs
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---------------
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| Full documentation on all methods, classes, installation methods, and integration setups for LangChain.
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- `Reference Documentation <./reference.html>`_
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.. toctree::
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:maxdepth: 1
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:caption: Reference
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:name: reference
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:hidden:
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./reference/installation.md
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./reference.rst
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Ecosystem
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------------
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| Guides for how other products can be used with LangChain.
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- `Integrations <./integrations.html>`_
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.. toctree::
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:maxdepth: 2
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:glob:
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:caption: Ecosystem
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:name: ecosystem
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:hidden:
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./integrations.rst
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Additional Resources
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---------------------
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| Additional resources we think may be useful as you develop your application!
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- `LangChainHub <https://github.com/hwchase17/langchain-hub>`_: The LangChainHub is a place to share and explore other prompts, chains, and agents.
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- `Gallery <https://github.com/kyrolabs/awesome-langchain>`_: A collection of great projects that use Langchain, compiled by the folks at `Kyrolabs <https://kyrolabs.com>`_. Useful for finding inspiration and example implementations.
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- `Deployments <./additional_resources/deployments.html>`_: A collection of instructions, code snippets, and template repositories for deploying LangChain apps.
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- `Tracing <./additional_resources/tracing.html>`_: A guide on using tracing in LangChain to visualize the execution of chains and agents.
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- `Model Laboratory <./additional_resources/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.
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- `Discord <https://discord.gg/6adMQxSpJS>`_: Join us on our Discord to discuss all things LangChain!
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- `YouTube <./additional_resources/youtube.html>`_: A collection of the LangChain tutorials and videos.
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- `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.
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.. toctree::
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:maxdepth: 1
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:caption: Additional Resources
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:name: resources
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:hidden:
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LangChainHub <https://github.com/hwchase17/langchain-hub>
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Gallery <https://github.com/kyrolabs/awesome-langchain>
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./additional_resources/deployments.md
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./additional_resources/tracing.md
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./additional_resources/model_laboratory.ipynb
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Discord <https://discord.gg/6adMQxSpJS>
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./additional_resources/youtube.md
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Production Support <https://forms.gle/57d8AmXBYp8PP8tZA>
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