RAG

🚀 Multimodal Chatbot for Efficient Retrieval-Augmented Generation (RAG)

📌 Project Overview

This project aims to develop an advanced multimodal chatbot that can efficiently retrieve and process information from various document formats—PDFs (with images/links), CSVs, voice files, and web links (including nested links). The extracted content is structured and stored in a vector database to support Retrieval-Augmented Generation (RAG), ensuring accurate, context-aware responses.

The system will also include automatic updates when documents are modified and provide full transparency into the retrieval, re-ranking, and LLM-generated responses.

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🎯 Objectives

✅ Seamless Data Ingestion – Extract meaningful content from diverse formats.
✅ Efficient Vector Storage & Retrieval – Use FAISS, Pinecone, or ChromaDB for optimized searches.
✅ Advanced RAG Implementation – Improve contextual understanding using Standard RAG, Graph RAG, or Agentic RAG.
✅ Real-time Updates – Automatically update the database when documents are modified.
✅ Explainability & Transparency – Display retrieval, re-ranking, and final responses for user trust.
✅ User-friendly UI/UX – Enable users to create and manage multiple document collections easily.
✅ API & Integration Support – Provide APIs for programmatic access.
✅ Security & Guardrails – Prevent hallucinations, misinformation, and unauthorized access.

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🚀 Key Challenges & Solutions

1️⃣ Data Ingestion & Preprocessing

• Extract text, images, and metadata from PDFs, CSVs, audio files, and web links (including nested links).
• Convert the extracted data into a structured format for vector storage.
• Handle complex documents with embedded images, hyperlinks, and voice transcripts.

2️⃣ Vector Database & RAG Implementation

• Store and retrieve data efficiently using FAISS, Pinecone, or ChromaDB.
• Implement Standard RAG, Graph RAG, or Agentic RAG for better contextual understanding.
• Enable real-time updates when documents in the collection are modified.

3️⃣ Retrieval Transparency & Explainability

• Clearly display:
  • Retrieved documents
  • Re-ranking results
  • Final LLM-generated responses
• Ensure transparency in chatbot decision-making.

4️⃣ User Interaction & UI/UX Considerations

• Multi-collection Support – Allow users to create and query different document sets.
• Intuitive UI – Provide an easy-to-use interface for document ingestion and chatbot interactions.

5️⃣ API & Integration Support

• Expose APIs for RAG-based retrieval, allowing external applications to fetch answers programmatically.

6️⃣ Security & Guardrails

• Prevent hallucinations, misinformation, and inappropriate responses.
• Ensure secure document handling and access control.