Software Engineering
12 learners
Building a RAG-Powered Chatbot with LangChain and Python
Transform your document collections into interactive chatbots with LangChain in Python. 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.
LangChain
OpenAI
Python
See path
4 lessons
18 practices
2 hours
Badge for RAG Systems and Vector Databases,
RAG Systems and Vector Databases
Lessons and practices
Lesson 1: Creating a Document Processor for Contextual Retrieval
Implementing Document Loading Logic
Initializing Vector Store and Retrieving Context
Processing Documents for Vector Storage
Building a Multi-Document Knowledge Base
Implementing Reset for Document Management
Lesson 2: Building a Chat Engine with Conversation History
Initializing the Chat Engine
Integrating Prompt Templates
Implementing the Send Message Method
Testing Chat Engine Without Context
Resetting Conversation History
Lesson 3: Integrating Components for a Complete RAG Chatbot
Implementing Document Upload and Error Handling
Handling User Messages and Retrieval
Enhancing Chatbot Context with Sources
Mastering Chatbot Reset Functions
Lesson 4: Analyzing Interplanetary Agreements with RAG
Querying a Single Interplanetary Agreement
Cosmic Treaty Comparison Challenge
Exploring the Document Multiverse
Final Mission: Isolate and Analyze Each Document
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