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docs[patch]: Update docs introduction and README (#23558)
CC @hwchase17 @baskaryan
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README.md
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README.md
@ -38,24 +38,25 @@ conda install langchain -c conda-forge
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For these applications, LangChain simplifies the entire application lifecycle:
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- **Open-source libraries**: Build your applications using LangChain's [modular building blocks](https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel) and [components](https://python.langchain.com/v0.2/docs/concepts/#components). Integrate with hundreds of [third-party providers](https://python.langchain.com/v0.2/docs/integrations/platforms/).
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- **Open-source libraries**: Build your applications using LangChain's open-source [building blocks](https://python.langchain.com/v0.2/docs/concepts#langchain-expression-language-lcel), [components](https://python.langchain.com/v0.2/docs/concepts), and [third-party integrations](https://python.langchain.com/v0.2/docs/integrations/platforms/).
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Use [LangGraph](/docs/concepts/#langgraph) to build stateful agents with first-class streaming and human-in-the-loop support.
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- **Productionization**: Inspect, monitor, and evaluate your apps with [LangSmith](https://docs.smith.langchain.com/) so that you can constantly optimize and deploy with confidence.
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- **Deployment**: Turn any chain into a REST API with [LangServe](https://python.langchain.com/v0.2/docs/langserve/).
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- **Deployment**: Turn your LangGraph applications into production-ready APIs and Assistants with [LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/).
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### Open-source libraries
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- **`langchain-core`**: Base abstractions and LangChain Expression Language.
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- **`langchain-community`**: Third party integrations.
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- Some integrations have been further split into **partner packages** that only rely on **`langchain-core`**. Examples include **`langchain_openai`** and **`langchain_anthropic`**.
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- **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
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- **[`LangGraph`](https://langchain-ai.github.io/langgraph/)**: A library for building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph.
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- **[`LangGraph`](https://langchain-ai.github.io/langgraph/)**: A library for building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it.
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### Productionization:
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- **[LangSmith](https://docs.smith.langchain.com/)**: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
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### Deployment:
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- **[LangServe](https://python.langchain.com/v0.2/docs/langserve/)**: A library for deploying LangChain chains as REST APIs.
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- **[LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/)**: Turn your LangGraph applications into production-ready APIs and Assistants.
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## 🧱 What can you build with LangChain?
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@ -106,7 +107,7 @@ Retrieval Augmented Generation involves [loading data](https://python.langchain.
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**🤖 Agents**
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Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete. LangChain provides a [standard interface for agents](https://python.langchain.com/v0.2/docs/concepts/#agents) along with the [LangGraph](https://github.com/langchain-ai/langgraph) extension for building custom agents.
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Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete. LangChain provides a [standard interface for agents](https://python.langchain.com/v0.2/docs/concepts/#agents), along with [LangGraph](https://github.com/langchain-ai/langgraph) for building custom agents.
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## 📖 Documentation
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@ -120,10 +121,9 @@ Please see [here](https://python.langchain.com) for full documentation, which in
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## 🌐 Ecosystem
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- [🦜🛠️ LangSmith](https://docs.smith.langchain.com/): Tracing and evaluating your language model applications and intelligent agents to help you move from prototype to production.
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- [🦜🕸️ LangGraph](https://langchain-ai.github.io/langgraph/): Creating stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain primitives.
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- [🦜🏓 LangServe](https://python.langchain.com/docs/langserve): Deploying LangChain runnables and chains as REST APIs.
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- [LangChain Templates](https://python.langchain.com/v0.2/docs/templates/): Example applications hosted with LangServe.
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- [🦜🛠️ LangSmith](https://docs.smith.langchain.com/): Trace and evaluate your language model applications and intelligent agents to help you move from prototype to production.
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- [🦜🕸️ LangGraph](https://langchain-ai.github.io/langgraph/): Create stateful, multi-actor applications with LLMs. Integrates smoothly with LangChain, but can be used without it.
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- [🦜🏓 LangServe](https://python.langchain.com/docs/langserve): Deploy LangChain runnables and chains as REST APIs.
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## 💁 Contributing
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@ -51,8 +51,8 @@ A developer platform that lets you debug, test, evaluate, and monitor LLM applic
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<ThemedImage
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alt="Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers."
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sources={{
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light: useBaseUrl('/svg/langchain_stack.svg'),
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dark: useBaseUrl('/svg/langchain_stack_dark.svg'),
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light: useBaseUrl('/svg/langchain_stack_june_2024.svg'),
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dark: useBaseUrl('/svg/langchain_stack_june_2024_dark.svg'),
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}}
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title="LangChain Framework Overview"
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/>
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@ -72,7 +72,7 @@ pip install langchain-experimental
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```
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### LangGraph
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`langgraph` is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain.
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`langgraph` is a library for building stateful, multi-actor applications with LLM. It integrates smoothly with LangChain, but can be used without it.
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Install with:
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```bash
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@ -8,9 +8,10 @@ sidebar_class_name: hidden
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**LangChain** is a framework for developing applications powered by large language models (LLMs).
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LangChain simplifies every stage of the LLM application lifecycle:
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- **Development**: Build your applications using LangChain's open-source [building blocks](/docs/concepts#langchain-expression-language-lcel) and [components](/docs/concepts). Hit the ground running using [third-party integrations](/docs/integrations/platforms/) and [Templates](/docs/templates).
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- **Development**: Build your applications using LangChain's open-source [building blocks](/docs/concepts#langchain-expression-language-lcel), [components](/docs/concepts), and [third-party integrations](/docs/integrations/platforms/).
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Use [LangGraph](/docs/concepts/#langgraph) to build stateful agents with first-class streaming and human-in-the-loop support.
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- **Productionization**: Use [LangSmith](https://docs.smith.langchain.com/) to inspect, monitor and evaluate your chains, so that you can continuously optimize and deploy with confidence.
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- **Deployment**: Turn any chain into an API with [LangServe](/docs/langserve).
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- **Deployment**: Turn your LangGraph applications into production-ready APIs and Assistants with [LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/).
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import ThemedImage from '@theme/ThemedImage';
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import useBaseUrl from '@docusaurus/useBaseUrl';
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@ -18,8 +19,8 @@ import useBaseUrl from '@docusaurus/useBaseUrl';
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<ThemedImage
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alt="Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers."
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sources={{
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light: useBaseUrl('/svg/langchain_stack.svg'),
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dark: useBaseUrl('/svg/langchain_stack_dark.svg'),
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light: useBaseUrl('/svg/langchain_stack_june_2024.svg'),
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dark: useBaseUrl('/svg/langchain_stack_june_2024_dark.svg'),
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}}
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title="LangChain Framework Overview"
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/>
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@ -30,7 +31,7 @@ Concretely, the framework consists of the following open-source libraries:
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- **`langchain-community`**: Third party integrations.
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- Partner packages (e.g. **`langchain-openai`**, **`langchain-anthropic`**, etc.): Some integrations have been further split into their own lightweight packages that only depend on **`langchain-core`**.
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- **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
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- **[LangGraph](https://langchain-ai.github.io/langgraph)**: Build robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph.
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- **[LangGraph](https://langchain-ai.github.io/langgraph)**: Build robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it.
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- **[LangServe](/docs/langserve)**: Deploy LangChain chains as REST APIs.
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- **[LangSmith](https://docs.smith.langchain.com)**: A developer platform that lets you debug, test, evaluate, and monitor LLM applications.
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@ -43,15 +44,17 @@ These docs focus on the Python LangChain library. [Head here](https://js.langcha
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## [Tutorials](/docs/tutorials)
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If you're looking to build something specific or are more of a hands-on learner, check out our [tutorials](/docs/tutorials).
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If you're looking to build something specific or are more of a hands-on learner, check out our [tutorials section](/docs/tutorials).
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This is the best place to get started.
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These are the best ones to get started with:
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- [Build a Simple LLM Application](/docs/tutorials/llm_chain)
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- [Build a Chatbot](/docs/tutorials/chatbot)
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- [Build an Agent](/docs/tutorials/agents)
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- [Introduction to LangGraph](https://langchain-ai.github.io/langgraph/tutorials/introduction/)
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Explore the full list of tutorials [here](/docs/tutorials).
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Explore the full list of LangChain tutorials [here](/docs/tutorials), and check out other [LangGraph tutorials here](https://langchain-ai.github.io/langgraph/tutorials/).
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## [How-to guides](/docs/how_to)
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@ -60,10 +63,14 @@ Explore the full list of tutorials [here](/docs/tutorials).
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These how-to guides don’t cover topics in depth – you’ll find that material in the [Tutorials](/docs/tutorials) and the [API Reference](https://api.python.langchain.com/en/latest/).
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However, these guides will help you quickly accomplish common tasks.
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Check out [LangGraph-specific how-tos here](https://langchain-ai.github.io/langgraph/how-tos/).
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## [Conceptual guide](/docs/concepts)
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Introductions to all the key parts of LangChain you’ll need to know! [Here](/docs/concepts) you'll find high level explanations of all LangChain concepts.
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For a deeper dive into LangGraph concepts, check out [this page](https://langchain-ai.github.io/langgraph/concepts/).
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## [API reference](https://api.python.langchain.com)
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Head to the reference section for full documentation of all classes and methods in the LangChain Python packages.
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@ -73,10 +80,7 @@ Head to the reference section for full documentation of all classes and methods
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Trace and evaluate your language model applications and intelligent agents to help you move from prototype to production.
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### [🦜🕸️ LangGraph](https://langchain-ai.github.io/langgraph)
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Build stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain primitives.
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### [🦜🏓 LangServe](/docs/langserve)
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Deploy LangChain runnables and chains as REST APIs.
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Build stateful, multi-actor applications with LLMs. Integrates smoothly with LangChain, but can be used without it.
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## Additional resources
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@ -15,7 +15,7 @@ LangChain Community contains third-party integrations that implement the base in
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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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@ -27,4 +27,4 @@ All changes will be accompanied by a patch version increase.
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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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For detailed information on how to contribute, see the [Contributing Guide](https://python.langchain.com/docs/contributing/).
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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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