chat-langchainjs

Introduction to Chat LangChain.js

Chat LangChain.js is a remarkable implementation designed to function as a locally hosted chatbot. Its primary focus is to answer questions concerning the LangChain documentation. This project skillfully merges the capabilities of LangChain with the dynamic framework of Next.js.

An operational version of this project is accessible via chatjs.langchain.com. For those interested in a Python variant, information can be found here.

Setting Up Locally

Steps for Local Development

To begin local development, follow these straightforward steps:

1. Install Dependencies: Use yarn install to set up all necessary dependencies.
2. Configure Environment: Define the environment variables required for the backend and frontend by referring to the samples provided in backend/.env.example and frontend/.env.example.

Ingestion Process

The ingestion process is critical for populating the chatbot with data, involving:

1. Backend Build: From the root directory, execute yarn build --filter=backend to compile the backend.
2. Run Ingestion Script: Enter the ./backend directory and run yarn ingest to initiate data ingestion.

Frontend Startup

Get the frontend up and running with these steps:

1. Start Frontend: Navigate to ./frontend and execute yarn dev.
2. Access the Application: Open localhost:3000 in your web browser to interact with the application.

Technical Architecture

Chat LangChain.js is engineered with two main components: ingestion and question-answering.

Ingestion Workflow

The ingestion workflow is a sequence of operations designed to gather and process data:

1. Data Collection: Retrieve HTML content from the documentation site and the GitHub codebase.
2. Loading Content: Use LangChain's RecursiveUrlLoader and SitemapLoader to load this data.
3. Document Splitting: Employ LangChain’s RecursiveCharacterTextSplitter to divide the documents into manageable parts.
4. Embedding Creation: Generate a vectorstore of embeddings using LangChain's Weaviate vectorstore wrapper, enriched with OpenAI’s embeddings.

Question-Answering Process

This component is designed to respond to user queries effectively:

1. Formulating Questions: Use GPT-3.5 to deduce standalone questions based on chat history and new input.
2. Document Retrieval: Search for pertinent documents within the vectorstore using the standalone question.
3. Answer Generation: These questions and documents are then processed by the model to produce and deliver a comprehensive answer.
4. Creating Trace URL: Generate a trace URL for the active chat session and configure an endpoint to gather user feedback.

Documentation for Users and Developers

For users and developers looking to utilize or tailor this project to their needs, several resources have been provided:

• Concepts: Offers an overview of the various components within Chat LangChain, covering features like ingestion, vector stores, and query analysis.
• Modify: A comprehensive guide to adapting Chat LangChain for specific requirements, including frontend and backend adjustments.
• Running Locally: Detailed instructions to operate Chat LangChain entirely on a local environment.
• LangSmith: Insights into enhancing application robustness using LangSmith, emphasizing observability, evaluations, and feedback.
• Production: Guidelines for preparing the application for production, addressing security considerations among other aspects.
• Deployment: Steps for deploying the application to a production environment, including database setup and frontend deployment.

This thorough introduction aims to provide a clear overview of the Chat LangChain.js project, from its development setup to its technical intricacies, making it approachable for both developers and users.