Upload AATS documents and ask questions. See how Retrieval-Augmented Generation works.
Upload documents (.txt, .md, or .pdf files) to build your knowledge base. The system will automatically chunk and index them for semantic search.
Ask questions in natural language. The system will find relevant passages from your documents using semantic similarity.
💡 How RAG Works: Your documents are split into chunks, converted to embeddings (vector representations), and stored in Qdrant. When you search, your query is also converted to an embedding, and the system finds the most semantically similar chunks using vector similarity.