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docs: core and community readme (#14623)
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⚡ Building applications with LLMs through composability ⚡
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[](https://github.com/langchain-ai/langchain/releases)
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[](https://github.com/langchain-ai/langchain/actions/workflows/langchain_ci.yml)
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[](https://github.com/langchain-ai/langchain/actions/workflows/langchain_experimental_ci.yml)
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[](https://github.com/langchain-ai/langchain/actions/workflows/check_diffs.yml)
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[](https://pepy.tech/project/langchain)
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[](https://opensource.org/licenses/MIT)
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[](https://twitter.com/langchainai)
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# langchain-community
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# 🦜️🧑🤝🧑 LangChain Community
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[](https://pepy.tech/project/langchain_community)
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[](https://opensource.org/licenses/MIT)
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## Quick Install
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```bash
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pip install langchain-community
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```
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## What is it?
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LangChain Community contains third-party integrations that implement the base interfaces defined in LangChain Core, making them ready-to-use in any LangChain application.
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For full documentation see the [API reference](https://api.python.langchain.com/en/stable/community_api_reference.html).
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## 📕 Releases & Versioning
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`langchain-community` is currently on version `0.0.x`
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All changes will be accompanied by a patch version increase.
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## 💁 Contributing
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As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
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For detailed information on how to contribute, see [here](.github/CONTRIBUTING.md).# langchain-community
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# langchain-core
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# 🦜🍎️ LangChain Core
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[](https://pepy.tech/project/langchain_core)
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[](https://opensource.org/licenses/MIT)
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## Quick Install
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```bash
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pip install langchain-core
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```
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## What is it?
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LangChain Core contains the base abstractions that power the rest of the LangChain ecosystem.
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These abstractions are designed to be as modular and simple as possible.
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Examples of these abstractions include those for language models, document loaders, embedding models, vectorstores, retrievers, and more.
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The benefit of having these abstractions is that any provider can implement the required interface and then easily be used in the rest of the LangChain ecosystem.
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For full documentation see the [API reference](https://api.python.langchain.com/en/stable/core_api_reference.html).
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## What is LangChain Expression Language?
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LangChain Core also contains LangChain Expression Language, or LCEL, a runtime that allows users to compose arbitrary sequences together and get several benefits that are important when building LLM applications.
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We call these sequences “runnables”.
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All runnables expose the same interface with single-invocation, batch, streaming and async methods.
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This design is useful because it is not enough to have a single sync interface when building an LLM application.
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Batch is needed for efficient processing of many inputs.
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Streaming (and streaming of intermediate steps) is needed to show the user that progress is being made.
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Async interfaces are nice when moving into production.
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Rather than having to write multiple implementations for all of those, LCEL allows you to write a runnable once and invoke it in many different ways.
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For more check out the [LCEL docs](https://python.langchain.com/docs/expression_language/).
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## 📕 Releases & Versioning
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`langchain-core` is currently on version `0.1.x`.
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As `langchain-core` contains the base abstractions and runtime for the whole LangChain ecosystem, we will communicate any breaking changes with advance notice and version bumps. The exception for this is anything in `langchain_core.beta`. The reason for `langchain_core.beta` is that given the rate of change of the field, being able to move quickly is still a priority, and this module is our attempt to do so.
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Minor version increases will occur for:
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- Breaking changes for any public interfaces NOT in `langchain_core.beta`
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Patch version increases will occur for:
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- Bug fixes
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- New features
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- Any changes to private interfaces
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- Any changes to `langchain_core.beta`
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## 💁 Contributing
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As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
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For detailed information on how to contribute, see [here](.github/CONTRIBUTING.md).
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