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:
| **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:
1. *Data-aware*: connect a language model to other sources of data
2. *Agentic*: allow a language model to interact with its environment
- *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 around these principles.
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/>`_.
| 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
----------------
How to get started using LangChain to create an Language Model application.
| How to get started using LangChain to create an Language Model application.
- `Getting Started tutorial<./getting_started/getting_started.html>`_
- `Concepts and terminology <./getting_started/concepts.html>`_
Tutorials created by community experts and presented on YouTube.
| Tutorials created by community experts and presented on YouTube.
- `Tutorials <./getting_started/tutorials.html>`_
@ -39,24 +38,26 @@ Tutorials created by community experts and presented on YouTube.
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:
| These modules are the core abstractions which we view as the building blocks of any LLM-powered application.
For each module LangChain provides standard, extendable interfaces. LanghChain also provides external integrations and even end-to-end implementations for off-the-shelf use.
| The docs for each module contain quickstart examples, how-to guides, reference docs, and conceptual guides.
- `Models <./modules/models.html>`_: The various model types and model integrations LangChain supports.
| The modules are (from least to most complex):
- `Prompts <./modules/prompts.html>`_: This includes prompt management, prompt optimization, and prompt serialization.
- `Models <./modules/models.html>`_: Supported model types and integrations.
- `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.
- `Prompts <./modules/prompts.html>`_: Prompt management, optimization, and serialization.
- `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.
- `Memory <./modules/memory.html>`_: Memory refers to state that is persisted between calls of a chain/agent.
- `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.
- `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.
- `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.
- `Chains <./modules/chains.html>`_: Chains are structured sequences of calls (to an LLM or to a different utility).
- `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.
- `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.
- `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.
..toctree::
:maxdepth:1
@ -75,29 +76,29 @@ These modules are, in increasing order of complexity:
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.
| Best practices and built-in implementations for common LangChain use cases:
- `Autonomous Agents <./use_cases/autonomous_agents.html>`_: Autonomous agents are longrunning agents that take many steps in an attempt to accomplish an objective. Examples include AutoGPT and BabyAGI.
- `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.
- `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.
- `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.
- `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.
- `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.
- `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.
- `Chatbots <./use_cases/chatbots.html>`_: Since language models are good at producing text, that makes them ideal for creating chatbots.
- `Chatbots <./use_cases/chatbots.html>`_: Language models love to chat, making this a very natural use of them.
- `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.
- `Querying Tabular Data <./use_cases/tabular.html>`_: Recommended reading if you want to use language models to query structured data (CSVs, SQL, dataframes, etc).
- `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.
- `Code Understanding <./use_cases/code.html>`_: Recommended reading if you want to use language models to analyze code.
- `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.
- `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.
- `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.
- `Summarization <./use_cases/summarization.html>`_: Compressing longer documents. 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.
- `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.
..toctree::
@ -106,9 +107,9 @@ The above modules can be used in a variety of ways. LangChain also provides guid
:name:use_cases
:hidden:
./use_cases/personal_assistants.md
./use_cases/autonomous_agents.md
./use_cases/agent_simulations.md
./use_cases/personal_assistants.md
./use_cases/question_answering.md
./use_cases/chatbots.md
./use_cases/tabular.rst
@ -122,7 +123,7 @@ The above modules can be used in a variety of ways. LangChain also provides guid
Reference Docs
---------------
All of LangChain's reference documentation, in one place. Full documentation on all methods, classes, installation methods, and integration setups for LangChain.
| Full documentation on all methods, classes, installation methods, and integration setups for LangChain.
- `Reference Documentation <./reference.html>`_
@ -140,7 +141,7 @@ All of LangChain's reference documentation, in one place. Full documentation on
LangChain Ecosystem
-------------------
Guides for how other companies/products can be used with LangChain
| Guides for how other companies/products can be used with LangChain.
- `LangChain Ecosystem <./ecosystem.html>`_
@ -157,7 +158,7 @@ Guides for how other companies/products can be used with LangChain
Additional Resources
---------------------
Additional collection of resources we think may be useful as you develop your application!
| Additional 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.
Davis Chase
1 year ago
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