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185 lines
8.1 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 via an API, but will also:
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- *Be data-aware*: connect a language model to other sources of data
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- *Be agentic*: allow a language model to interact with its environment
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The LangChain framework is designed with the above principles in mind.
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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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Checkout the below guide for a walkthrough of how to get started using LangChain to create an Language Model application.
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- `Getting Started Documentation <./getting_started/getting_started.html>`_
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.. toctree::
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:maxdepth: 1
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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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Modules
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-----------
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There are several main modules that LangChain provides support for.
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For each module we provide some examples to get started, how-to guides, reference docs, and conceptual guides.
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These modules are, in increasing order of complexity:
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- `Models <./modules/models.html>`_: The various model types and model integrations LangChain supports.
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- `Prompts <./modules/prompts.html>`_: This includes prompt management, prompt optimization, and prompt serialization.
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- `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.
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- `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.
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- `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.
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- `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.
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- `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.
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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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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.
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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 or to events can be an interesting way to observe their long-term memory abilities.
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- `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.
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- `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.
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- `Chatbots <./use_cases/chatbots.html>`_: Since language models are good at producing text, that makes them ideal for creating chatbots.
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- `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.
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- `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.
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- `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.
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- `Extraction <./use_cases/extraction.html>`_: Extract structured information from text.
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- `Summarization <./use_cases/summarization.html>`_: Summarizing longer documents into shorter, more condensed chunks of information. A type of Data Augmented Generation.
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- `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.
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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/personal_assistants.md
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./use_cases/autonomous_agents.md
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./use_cases/agent_simulations.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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All of LangChain's reference documentation, in one place. 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/integrations.md
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./reference.rst
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LangChain Ecosystem
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-------------------
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Guides for how other companies/products can be used with LangChain
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- `LangChain Ecosystem <./ecosystem.html>`_
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.. toctree::
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:maxdepth: 1
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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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./ecosystem.rst
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Additional Resources
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---------------------
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Additional collection of 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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- `Glossary <./glossary.html>`_: A glossary of all related terms, papers, methods, etc. Whether implemented in LangChain or not!
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- `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.
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- `Deployments <./deployments.html>`_: A collection of instructions, code snippets, and template repositories for deploying LangChain apps.
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- `Tracing <./tracing.html>`_: A guide on using tracing in LangChain to visualize the execution of chains and agents.
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- `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.
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- `Discord <https://discord.gg/6adMQxSpJS>`_: Join us on our Discord to discuss all things LangChain!
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- `YouTube <./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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./glossary.md
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./gallery.rst
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./deployments.md
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./tracing.md
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./use_cases/model_laboratory.ipynb
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Discord <https://discord.gg/6adMQxSpJS>
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./youtube.md
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Production Support <https://forms.gle/57d8AmXBYp8PP8tZA>
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