AI-Powered Knowledge Retrieval System

An advanced system combining Google Generative AI and LangChain for intelligent content analysis and question answering.

Vector Storage

Efficient storage and retrieval of document embeddings

AI Processing

Powered by Google's Gemini model for advanced text generation and analysis

Web Analysis

Process and analyze content from any webpage using CheerioWebBaseLoader

RAG Pipeline

Retrieval-augmented generation for accurate and contextual responses

How It Works

1

Document Processing

Web content is loaded and processed using LangChain's document loaders and text splitters

2

Vector Embedding

Content is transformed into vector embeddings for efficient similarity search

3

Knowledge Retrieval

Questions trigger relevant context retrieval from the vector store

4

AI Generation

Google's Gemini model generates accurate answers based on retrieved context

Technical Stack

  • Google Generative AI (Gemini model)
  • LangChain for document processing and RAG workflows
  • Vector embeddings for semantic search
  • StateGraph for orchestrating the retrieval pipeline
  • CheerioWebBaseLoader for web content extraction
  • Next.js and React for the user interface