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AI/ML2024
LLM Chatbot with RAG Pipeline
Context-aware AI assistant with custom knowledge base
Project Overview
An enterprise-ready chatbot solution powered by RAG architecture. Users can ingest any document corpus — PDFs, web pages, databases — and query it through natural language with GPT-4 or Claude as the generation backbone.
The system maintains conversational context across sessions, cites source documents for every answer, and supports both private and shared knowledge bases. Built with LangChain orchestration and Pinecone for vector search.
- 10+
- Doc Types
- 128K
- Context Window
- 100%
- Source Citation
- Streamed
- Response
Gallery
Key Features
- RAG with Pinecone vector database
- Streaming responses with SSE
- Multi-turn conversation memory
- Document ingestion pipeline (PDF, CSV, HTML)
- Source citation with relevance scores
Technology Stack
PythonLangChainOpenAIPineconeFastAPIReactTypeScript
