Automate Document QA with RAG Using Milvus, Cohere, and OpenAI for Google Drive

The workflow is designed to streamline the process of building a document-based QA system. It begins by watching a Google Drive folder for new PDF files, downloading and extracting their content. The extracted text is then split into chunks and vector embeddings are generated using Cohere. These embeddings are stored in a Milvus vector database, which the RAG agent queries to retrieve relevant information. The agent uses OpenAI to generate responses based on the retrieved data, creating a powerful tool for context-aware question answering.