mirror of
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f7dbdab5ba
Fix several docstring inaccuracies in langchain-core and update outdated LangSmith URLs across three README files. **Docstring fixes (libs/core):** - `tap_output_iter`: docstring says "async iterator" but method accepts sync `Iterator` - `agenerate_from_stream`: docstring says "Iterator" but method accepts `AsyncIterator` - `BaseLLM.OutputType`: docstring says "input type" but property returns output type - Grammar: "or deprecated" → "or be deprecated", "relies" → "rely", "whose the" → "whose" **URL fixes (libs/core, libs/langchain, libs/langchain_v1):** - Updated `smith.langchain.com` → `www.langchain.com/langsmith` (root README already uses the correct URL) Verified with `make lint` and `make format` in libs/core — no new issues introduced. Changes are docs-only with no code logic impact. *This PR was created with assistance from an AI coding tool.*
48 lines
2.7 KiB
Markdown
48 lines
2.7 KiB
Markdown
# 🦜🍎️ LangChain Core
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[](https://pypi.org/project/langchain-core/#history)
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[](https://opensource.org/licenses/MIT)
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[](https://pypistats.org/packages/langchain-core)
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[](https://x.com/langchain)
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Looking for the JS/TS version? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
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To help you ship LangChain apps to production faster, check out [LangSmith](https://www.langchain.com/langsmith).
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[LangSmith](https://www.langchain.com/langsmith) is a unified developer platform for building, testing, and monitoring LLM applications.
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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 this?
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LangChain Core contains the base abstractions that power the LangChain ecosystem.
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These abstractions are designed to be as modular and simple as possible.
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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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## ⛰️ Why build on top of LangChain Core?
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The LangChain ecosystem is built on top of `langchain-core`. Some of the benefits:
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- **Modularity**: We've designed Core 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**: Core components have the largest install base in the LLM ecosystem, and are used in production by many companies.
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## 📖 Documentation
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For full documentation, see the [API reference](https://reference.langchain.com/python/langchain_core/). For conceptual guides, tutorials, and examples on using LangChain, see the [LangChain Docs](https://docs.langchain.com/oss/python/langchain/overview). You can also chat with the docs using [Chat LangChain](https://chat.langchain.com).
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## 📕 Releases & Versioning
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See our [Releases](https://docs.langchain.com/oss/python/release-policy) and [Versioning](https://docs.langchain.com/oss/python/versioning) policies.
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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://docs.langchain.com/oss/python/contributing/overview).
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