Retrieval-Augmented Generation (RAG) enhances AI responses with relevant information from your document collections and knowledge bases.
Features
- 📄Document Upload - Upload and manage documents for AI retrieval
- 🔢Vector Embeddings - Automatic creation of vector embeddings for semantic search
- 🎯Retrieval Strategies - Configure how information is retrieved
- 📁Collections - Organize documents into knowledge collections
How RAG Works
- Upload Documents - Add your PDFs, text files, or other documents
- Processing - Documents are chunked and embedded into vectors
- Storage - Vectors are stored in the knowledge base
- Query - When you ask a question, relevant chunks are retrieved
- Generation - AI generates a response using the retrieved context
💡 Pro Tip
Organize documents into collections by topic for more accurate retrieval. Use descriptive names for your collections.