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Building a RAG-Powered Chatbot with LangChain and TypeScript

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Overview

Transform your document collections into interactive chatbots with LangChain in TypeScript. Build a complete RAG (Retrieval-Augmented Generation) system by integrating document processing, contextual retrieval, and conversational memory. Develop chatbots that deliver precise information from documents, enabling applications like legal document analysis and querying.

Syllabus

Unit 1: Creating a Document Processor for Contextual Retrieval

Implement Document Loading Functionality
Initializing Vector Store and Implementing Context Retrieval
Implementing Document Processing and Retrieval
Processing Multiple Documents with Vector Store
Implementing a Reset Method for the Document Processor

Unit 2: Building a Chat Engine with Conversation History

Implementing the ChatEngine Constructor
Implementing Prompt Templates in the Chat Engine
Implement Message Handling in the Chat Engine
Testing the Chat Engine Without Context
Implementing Conversation History Reset

Unit 3: Integrating Components for a Complete RAG Chatbot

Implementing Document Upload Functionality in the RAG Chatbot
Implementing Context Retrieval and Chat Engine Communication
Including Document Source Information in Chatbot Responses
Implementing Reset Functionality for Chatbot State Management

Unit 4: Analyzing Interplanetary Agreements with RAG

Analyzing Interplanetary Trade Agreements with RAG
Analyzing Interstellar Diplomatic Documents with RAG
Navigating the Document Multiverse with RAG
Batch Processing Documents with RAG Chatbot

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