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# You will need to get your own API key. See

api_key = ""
from langchain.document_loaders import ToMarkdownLoader
loader = ToMarkdownLoader.from_api_key(
url="", api_key=api_key
docs = loader.load()
    ## Contents

- [Getting Started](#getting-started)
- [Modules](#modules)
- [Use Cases](#use-cases)
- [Reference Docs](#reference-docs)
- [LangChain Ecosystem](#langchain-ecosystem)
- [Additional Resources](#additional-resources)

## Welcome to LangChain [\#](\#welcome-to-langchain "Permalink to this headline")

**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

The LangChain framework is designed around these principles.

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 [\#](\#getting-started "Permalink to this headline")

How to get started using LangChain to create an Language Model application.

- [Quickstart Guide](

Concepts and terminology.

- [Concepts and terminology](

Tutorials created by community experts and presented on YouTube.

- [Tutorials](

## Modules [\#](\#modules "Permalink to this headline")

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.

The modules are (from least to most complex):

- [Models]( Supported model types and integrations.

- [Prompts]( Prompt management, optimization, and serialization.

- [Memory]( Memory refers to state that is persisted between calls of a chain/agent.

- [Indexes]( 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.

- [Chains]( Chains are structured sequences of calls (to an LLM or to a different utility).

- [Agents]( 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]( 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.

## Use Cases [\#](\#use-cases "Permalink to this headline")

Best practices and built-in implementations for common LangChain use cases:

- [Autonomous Agents]( Autonomous agents are long-running agents that take many steps in an attempt to accomplish an objective. Examples include AutoGPT and BabyAGI.

- [Agent Simulations]( 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]( One of the primary LangChain use cases. Personal assistants need to take actions, remember interactions, and have knowledge about your data.

- [Question Answering]( Another common LangChain use case. Answering questions over specific documents, only utilizing the information in those documents to construct an answer.

- [Chatbots]( Language models love to chat, making this a very natural use of them.

- [Querying Tabular Data]( Recommended reading if you want to use language models to query structured data (CSVs, SQL, dataframes, etc).

- [Code Understanding]( Recommended reading if you want to use language models to analyze code.

- [Interacting with APIs]( 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]( Extract structured information from text.

- [Summarization]( Compressing longer documents. A type of Data-Augmented Generation.

- [Evaluation]( Generative models are hard to evaluate with traditional metrics. One promising approach is to use language models themselves to do the evaluation.

## Reference Docs [\#](\#reference-docs "Permalink to this headline")

Full documentation on all methods, classes, installation methods, and integration setups for LangChain.

- [Reference Documentation](

## LangChain Ecosystem [\#](\#langchain-ecosystem "Permalink to this headline")

Guides for how other companies/products can be used with LangChain.

- [LangChain Ecosystem](

## Additional Resources [\#](\#additional-resources "Permalink to this headline")

Additional 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]( A collection of our favorite projects that use LangChain. Useful for finding inspiration or seeing how things were done in other applications.

- [Deployments]( A collection of instructions, code snippets, and template repositories for deploying LangChain apps.

- [Tracing]( A guide on using tracing in LangChain to visualize the execution of chains and agents.

- [Model Laboratory]( 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]( 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.