RAG with andOpenAI

How it works

1. 1.Activate manual trigger or receive webhook request to initiate the workflow
2. 2.Extract documents from Google Drive and process them using the default data loader
3. 3.Segment documents into manageable chunks with the token-based text splitter
4. 4.Embed chunks via OpenAI models and store them in Qdrant vector database
5. 5.Run OpenAI agent using vector store as a retrieval tool for contextual search
6. 6.Respond to user queries based on retrieved context while preserving chat memory

Use cases

• Question-answering system built on internal company documents and knowledge bases
• Intelligent assistant that understands multiple cloud-stored files and answers queries with precise context
• Advanced semantic search application that retrieves by meaning rather than keywords
• Interactive report generator that converses with users to extract required information

Requirements

• Valid OpenAI API key for GPT models and embedding services
• Proper Google Drive access credentials with file read permissions
• Configured Qdrant server ready to receive, store, and search vector embeddings
• Basic understanding of RAG mechanisms and large-scale text data processing

Service Value

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