RAG Chatbot

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  home page

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  RAG feature

Inspiration

Growing enterprise knowledge bases demand intelligent search beyond traditional methods. RAG (Retrieval-Augmented Generation) offers a solution by combining document retrieval with AI-powered natural language understanding.

What it does

Processes and indexes enterprise documents Provides natural language search interface Generates contextually relevant responses using RAG Maintains source traceability for all responses Integrates with existing document management systems

How we built it

Built using:

Streamlit for web interface LangChain for RAG implementation Pinecone for vector storage and similarity search OpenAI embeddings for document vectorization

Architecture flow:

Document ingestion and chunking Vector embedding generation Storage in Pinecone index Query processing via LangChain Response generation and display through Streamlit

Challenges we ran into

Optimizing vector search performance at scale Maintaining response accuracy with growing document base Balancing between response latency and quality Implementing efficient document chunking strategies

Accomplishments that we're proud of

Achieved sub-second response times Developed scalable document processing pipeline Created accurate citation system Implemented efficient caching mechanisms

What we learned

RAG architecture optimization techniques Vector database performance tuning Large-scale document processing strategies Enterprise system integration patterns

What's next for RAG Chatbot

Moondream Integration Multi-language support Real-time document updates Advanced document type handling Enhanced security features API integration capabilities

Built With

• amazon-web-services
• langchain
• pinecone
• python
• streamlit