Automated Document QA System with RAG Using Milvus, Cohere, OpenAI & Google Driv

Overview

This workflow automates the creation of a document Question Answering (QA) system by integrating Google Drive, Milvus, Cohere, and OpenAI. It leverages Retrieval-Augmented Generation (RAG) to provide intelligent, context-aware answers from your document repository.

Key Features

• Automated Document Ingestion: Monitors Google Drive for new files and automatically downloads them.
• Data Processing & Embedding: Uses Cohere to generate embeddings for document content, optimizing semantic search.
• Vector Storage: Stores embeddings in Milvus for scalable, high-performance retrieval.
• RAG Agent: Employs a RAG agent to retrieve relevant document chunks and generate answers using OpenAI's GPT-4o.
• Chat Integration: Supports real-time chat-based queries, maintaining context with memory buffers.

Benefits

• Enhanced Productivity: Automates the entire document QA pipeline, reducing manual effort.
• Accurate Answers: Delivers precise, context-rich responses by combining retrieval and generation.
• Seamless Integration: Connects leading AI and storage platforms for robust, scalable solutions.
• Time Savings: Rapidly processes and indexes new documents, ensuring up-to-date knowledge bases.

Use Cases

• Internal knowledge base automation
• Customer support document search
• Legal or compliance document Q&A
• Research and academic information retrieval