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Building a RAG-Powered Chatbot with LangChain and Java

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Overview

Transform your document collections into interactive chatbots with LangChain in Java. 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 document analysis and querying.

Syllabus

Unit 1: Creating a Document Processor for Contextual Retrieval in Java

Implement PDF Document Loading Functionality
Implementing Vector Store and Context Retrieval
Implementing Document Processing and Retrieval for RAG Systems
Multi-Document Processing and Retrieval
Implementing Vector Store Reset Functionality

Unit 2: Building a Chat Engine with Conversation History in Java

Implementing the ChatEngine Constructor
Implementing Prompt Templates in the Chat Engine
Implementing Message Handling in a Chat Engine
Testing sendMessage Method Without Context
Implementing Conversation Reset Feature in Chat Engine

Unit 3: Integrating Components for a Complete RAG Chatbot in Java

Implementing Document Upload Functionality in RAG Chatbot
Implementing Message Processing for RAG Chatbot
Enhancing RAG Chatbot with Source Attribution
Implementing Reset Functionality in a RAG Chatbot

Unit 4: Analyzing Interplanetary Agreements with RAG in Java

Interstellar Document Analysis with RAG Chatbot
Implementing Cross-Document Analysis for Space Treaties with RAG
Processing Multiple Documents with RAG System
Processing Multiple Documents with RAG Chatbot

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