Building a Real-Time RAG System: HTML Processing with LangGraph and Streaming Capabilities

An advanced implementation of a Retrieval-Augmented Generation (RAG) system that processes HTML content with state-of-the-art features including real-time streaming, stateful operations, and efficient document processing.
Key Features
HTML Content Processing: Utilizes WebBaseLoader for efficient web page content extraction
Smart Document Chunking: Implements RecursiveCharacterTextSplitter for intelligent document segmentation
In-Memory Vector Storage: Leverages InMemoryStore for fast vector operations
Stateful Operations: Incorporates LangGraph for maintaining conversation state and context
Real-Time Streaming: Features streaming capabilities for immediate response generation
Advanced Retrieval System: Custom retriever implementation using LangGraph
Technical Implementation Details
The system architecture combines several cutting-edge components:
Document Processing Pipeline
WebBaseLoader handles HTML content extraction
RecursiveCharacterTextSplitter ensures context-aware document chunking
Documents are processed and stored in an InMemoryStore for quick access
LangGraph Integration
Implements stateful conversation management
Handles complex conversation flows and context retention
Enables seamless integration of retrieval and generation components
Streaming Architecture
Real-time response generation capabilities
Efficient memory usage during streaming operations
Immediate feedback loop for enhanced user experience
Retrieval System
Custom retriever implementation using LangGraph
Optimized for relevance and response time
Seamless integration with the generation pipeline
Applications
Real-time customer support systems
Documentation search and retrieval
Interactive chatbots with web knowledge
Dynamic content generation systems
Educational platforms with real-time responses
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