From 4c8e184cbc4c7f21f96c75b6f8d354ef59e30532 Mon Sep 17 00:00:00 2001 From: Jacob Lee Date: Sun, 30 Jun 2024 22:16:58 -0700 Subject: [PATCH] docs[patch]: Update README.md (#5945) * docs[patch]: Update README.md * Update readmes * Format --- README.md | 7 ------- langchain/README.md | 23 +++++++++-------------- 2 files changed, 9 insertions(+), 21 deletions(-) diff --git a/README.md b/README.md index 98e4d6ca6f03..722d755b5c2f 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,6 @@ Looking for the Python version? Check out [LangChain](https://github.com/langcha To help you ship LangChain apps to production faster, check out [LangSmith](https://smith.langchain.com). [LangSmith](https://smith.langchain.com) is a unified developer platform for building, testing, and monitoring LLM applications. -Fill out [this form](https://airtable.com/appwQzlErAS2qiP0L/shrGtGaVBVAz7NcV2) to get on the waitlist or speak with our sales team. ## ⚡️ Quick Install @@ -17,10 +16,6 @@ You can use npm, yarn, or pnpm to install LangChain.js `npm install -S langchain` or `yarn add langchain` or `pnpm add langchain` -```typescript -import { ChatOpenAI } from "langchain/chat_models/openai"; -``` - ## 🌐 Supported Environments LangChain is written in TypeScript and can be used in: @@ -61,7 +56,6 @@ This library aims to assist in the development of those types of applications. C - [Documentation](https://js.langchain.com/v0.2/docs/tutorials/rag/) - End-to-end Example: [Doc-Chatbot](https://github.com/dissorial/doc-chatbot) - **💬 Chatbots** - [Documentation](https://js.langchain.com/v0.2/docs/tutorials/chatbot) @@ -109,4 +103,3 @@ Please report any security issues or concerns following our [security guidelines ## 🖇️ Relationship with Python LangChain This is built to integrate as seamlessly as possible with the [LangChain Python package](https://github.com/langchain-ai/langchain). Specifically, this means all objects (prompts, LLMs, chains, etc) are designed in a way where they can be serialized and shared between languages. - diff --git a/langchain/README.md b/langchain/README.md index 6980c27c318d..5bfbb1ba93a2 100644 --- a/langchain/README.md +++ b/langchain/README.md @@ -9,7 +9,6 @@ Looking for the Python version? Check out [LangChain](https://github.com/langcha To help you ship LangChain apps to production faster, check out [LangSmith](https://smith.langchain.com). [LangSmith](https://smith.langchain.com) is a unified developer platform for building, testing, and monitoring LLM applications. -Fill out [this form](https://airtable.com/appwQzlErAS2qiP0L/shrGtGaVBVAz7NcV2) to get off the waitlist or speak with our sales team. ## ⚡️ Quick Install @@ -17,10 +16,6 @@ You can use npm, yarn, or pnpm to install LangChain.js `npm install -S langchain` or `yarn add langchain` or `pnpm add langchain` -```typescript -import { ChatOpenAI } from "langchain/chat_models/openai"; -``` - ## 🌐 Supported Environments LangChain is written in TypeScript and can be used in: @@ -39,15 +34,16 @@ LangChain is written in TypeScript and can be used in: - **Reason**: rely on a language model to reason (about how to answer based on provided context, what actions to take, etc.) This framework consists of several parts. -- **LangChain Libraries**: The Python and JavaScript libraries. Contains interfaces and integrations for a myriad of components, a basic runtime for combining these components into chains and agents, and off-the-shelf implementations of chains and agents. -- **[LangChain Templates](https://github.com/langchain-ai/langchain/tree/master/templates)**: (currently Python-only) A collection of easily deployable reference architectures for a wide variety of tasks. -- **[LangServe](https://github.com/langchain-ai/langserve)**: (currently Python-only) A library for deploying LangChain chains as a REST API. -- **[LangSmith](https://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. +- **Open-source libraries**: Build your applications using LangChain's open-source [building blocks](https://js.langchain.com/v0.2/docs/concepts#langchain-expression-language), [components](https://js.langchain.com/v0.2/docs/concepts), and [third-party integrations](https://js.langchain.com/v0.2/docs/integrations/platforms/). +Use [LangGraph.js](https://js.langchain.com/v0.2/docs/concepts/#langgraphjs) to build stateful agents with first-class streaming and human-in-the-loop support. +- **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. +- **Deployment**: Turn your LangGraph applications into production-ready APIs and Assistants with [LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/) (currently Python-only). The LangChain libraries themselves are made up of several different packages. - **[`@langchain/core`](https://github.com/langchain-ai/langchainjs/blob/main/langchain-core)**: Base abstractions and LangChain Expression Language. - **[`@langchain/community`](https://github.com/langchain-ai/langchainjs/blob/main/libs/langchain-community)**: Third party integrations. - **[`langchain`](https://github.com/langchain-ai/langchainjs/blob/main/langchain)**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture. +- **[LangGraph.js](https://langchain-ai.github.io/langgraphjs/)**: 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. Integrations may also be split into their own compatible packages. @@ -86,15 +82,15 @@ Data Augmented Generation involves specific types of chains that first interact **🤖 Agents:** -Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end-to-end agents. +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://js.langchain.com/v0.2/docs/concepts/#agents), along with [LangGraph.js](https://github.com/langchain-ai/langgraphjs/) for building custom agents. ## 📖 Documentation -Please see [here](https://js.langchain.com/v0.2/) for full documentation, which includes: +Please see [here](https://js.langchain.com) for full documentation, which includes: - [Getting started](https://js.langchain.com/v0.2/docs/introduction): installation, setting up the environment, simple examples -- [Tutorials](https://js.langchain.com/v0.2/docs/tutorials/): interactive guides and walkthroughs of common use cases/tasks. -- [Use case](https://js.langchain.com/v0.2/docs/how_to/) walkthroughs and best practices for every component of the LangChain library. +- Overview of the [interfaces](https://js.langchain.com/v0.2/docs/how_to/lcel_cheatsheet/), [modules](https://js.langchain.com/v0.2/docs/concepts) and [integrations](https://js.langchain.com/v0.2/docs/integrations/platforms/) +- [Tutorial](https://js.langchain.com/v0.2/docs/tutorials/) walkthroughs - [Reference](https://api.js.langchain.com): full API docs ## 💁 Contributing @@ -108,4 +104,3 @@ Please report any security issues or concerns following our [security guidelines ## 🖇️ Relationship with Python LangChain This is built to integrate as seamlessly as possible with the [LangChain Python package](https://github.com/langchain-ai/langchain). Specifically, this means all objects (prompts, LLMs, chains, etc) are designed in a way where they can be serialized and shared between languages. -