mirror of
https://github.com/hwchase17/langchain
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91 lines
4.9 KiB
Markdown
91 lines
4.9 KiB
Markdown
# 🦜🍎️ 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. 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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## 1️⃣ Core Interface: Runnables
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The concept of a Runnable is central to LangChain Core – it is the interface that most LangChain Core components implement, giving them
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- a common invocation interface (invoke, batch, stream, etc.)
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- built-in utilities for retries, fallbacks, schemas and runtime configurability
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- easy deployment with [LangServe](https://github.com/langchain-ai/langserve)
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For more check out the [runnable docs](https://python.langchain.com/docs/expression_language/interface). Examples of components that implement the interface include: LLMs, Chat Models, Prompts, Retrievers, Tools, Output Parsers.
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You can use LangChain Core objects in two ways:
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1. **imperative**, ie. call them directly, eg. `model.invoke(...)`
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2. **declarative**, with LangChain Expression Language (LCEL)
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3. or a mix of both! eg. one of the steps in your LCEL sequence can be a custom function
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| Feature | Imperative | Declarative |
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| --------- | ------------------------------- | -------------- |
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| Syntax | All of Python | LCEL |
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| Tracing | ✅ – Automatic | ✅ – Automatic |
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| Parallel | ✅ – with threads or coroutines | ✅ – Automatic |
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| Streaming | ✅ – by yielding | ✅ – Automatic |
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| Async | ✅ – by writing async functions | ✅ – Automatic |
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## ⚡️ What is LangChain Expression Language?
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LangChain Expression Language (LCEL) is a _declarative language_ for composing LangChain Core runnables into sequences (or DAGs), covering the most common patterns when building with LLMs.
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LangChain Core compiles LCEL sequences to an _optimized execution plan_, with automatic parallelization, streaming, tracing, and async support.
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For more check out the [LCEL docs](https://python.langchain.com/docs/expression_language/).
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For more advanced use cases, also check out [LangGraph](https://github.com/langchain-ai/langgraph), which is a graph-based runner for cyclic and recursive LLM workflows.
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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 the [Contributing Guide](https://python.langchain.com/docs/contributing/).
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## ⛰️ Why build on top of LangChain Core?
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The whole LangChain ecosystem is built on top of LangChain Core, so you're in good company when building on top of it. Some of the benefits:
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- **Modularity**: LangChain Core is designed around abstractions that are independent of each other, and not tied to any specific model provider.
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- **Stability**: We are committed to a stable versioning scheme, and will communicate any breaking changes with advance notice and version bumps.
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- **Battle-tested**: LangChain Core components have the largest install base in the LLM ecosystem, and are used in production by many companies.
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- **Community**: LangChain Core is developed in the open, and we welcome contributions from the community.
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