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Library / RAG & Knowledge Bases

RAG & Knowledge Bases

RAG Agent with Milvus, Cohere and OpenAI for Drive Files

When new files appear in Google Drive they are embedded with Cohere into Milvus, and when a chat message arrives an OpenAI agent answers from them.

What it does

## Why Milvus Based on comparisons and user feedback, Milvus is often considered a more performant and scalable vector database solution compared to Supabase, particularly for demanding use cases involving large datasets, high-volume vector search operations, and multilingual support. ### Requirements - Create an account on Zilliz to generate the Milvus cluster. - There is no need to create docker containers or your own instance, Zilliz provides the cloud infraestructure to build it easily - Get your credentials ready from Drive, Milvus (Zilliz), and Cohere ### Usage Every time a new pdf is added into the Drive folder, it will be inserted into the Milvus Vector Store, allowing for the interaction with the RAG agent in seconds. ## Calculate your company's RAG costs Want to run Milvus on your own server on n8n? Zilliz provides a great cost calculator ### Get in touch with us Want to implement a RAG AI agent for your company? Shoot us a message

The steps

  1. Default Data Loader
  2. Embeddings Cohere
  3. When chat message received
  4. Watch New Files
  5. Download New
  6. Insert into Milvus
  7. RAG Agent
  8. Retrieve from Milvus
  9. OpenAI 4o
  10. Memory
  11. Cohere embeddings
  12. Set Chunks
  13. Extract from File

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