RAG AI agent with and

How it works

1. 1.Load documents from Google Drive and split them using recursive character text processing
2. 2.Create document embeddings with Cohere and store vectors in Milvus database
3. 3.Receive user queries through chat interface and convert them to search operations
4. 4.Search matching document vectors and retrieve relevant contextual information
5. 5.Feed context and query to OpenAI agent with buffered conversation memory
6. 6.Send generated response to user through the chat channel

Use cases

• Intelligent customer support system that answers questions based on stored knowledge base
• Internal search engine that clarifies company information and policies for employees
• Document analysis assistant that extracts insights from multiple files simultaneously
• Interactive learning platform that answers student questions from course materials

Requirements

• Google Drive account containing documents for processing and analysis
• Valid API keys for OpenAI and Cohere services
• Running Milvus vector database server accessible to the workflow
• Supported file formats (PDF, DOCX, TXT) in the source folder

Service Value

Ideal as a smart automation service combining integrations and AI to produce ready-to-use results.