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RAG & Knowledge Bases

Cut RAG Costs with a Q&A Tool and Multiple Retrieval Steps

When a file is uploaded it is embedded into a vector store, and when a chat message arrives an OpenAI agent answers through a cheaper question-answer tool.

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What it does

### Readme Load your data into a vector database with the 📚 Load Data flow, and then use your data as chat context with the 🐕 Retriever flow. Quick start 1. Click on the Execute Workflow button to run the 📚 Load Data flow. 2. Click on Open Chat button to run the 🐕 Retriever flow. Then ask a question about content from your document(s) For more info, check our docs on RAG in n8n

The steps

  1. Upload your file here
  2. Embeddings OpenAI
  3. Default Data Loader
  4. Insert Data to Store
  5. Query Data Tool
  6. AI Agent
  7. When chat message received
  8. Answer questions with a vector store
  9. Expensive model
  10. Cheap Model

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