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>`_
Concepts and terminology.
- `Concepts and terminology <./getting_started/concepts.html>`_
Tutorials created by community experts and presented on YouTube.
- `Tutorials <./getting_started/tutorials.html>`_
.. toctree::
:maxdepth: 2
:caption: Getting Started
:name: getting_started
:hidden:
getting_started/getting_started.md
getting_started/concepts.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.
- `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