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Building a RAG-Powered Chatbot with LangChain and Go

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Overview

Transform your document collections into interactive chatbots with LangChain in Go. 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: Building a Document Processor

Implement PDF Document Loader in Go
Initialize DocumentProcessor Vector Store and Retrieve Context in Go
Go Document Processor with OpenAI Integration
Process Multiple Documents in Go
Reset the Document Processor Vector Store

Unit 2: Building a Chat Engine

Initialize ChatEngine in Go
Integrate Explicit System and Human Message Templates in Go Chat Engine
Implement the SendMessage Method for ChatEngine in Go
Call SendMessage Without Context
Reset Chat Conversation History

Unit 3: Integrating the RAG Chatbot

Implement UploadDocument Method for RAGChatbot in Go
Implement SendMessage Method for Go RAGChatbot
Include Source Metadata in SendMessage Context
Implement Reset Functionality for RAG Chatbot in Go

Unit 4: Analyzing Agreements with RAG

Interplanetary Trade Agreement Analysis with Go RAG Chatbot
Galactic Accord Comparative Analysis in Go
Traverse the Document Multiverse with a RAG Chatbot in Go
Batch Process and Query Documents with Go RAGChatbot

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