4-Step Vector Database Workflow (RAG)
1. Query
User asks a question
2. Semantic Search
Query converted to Vector & searched in DB
3. Context Retrieval (RAG)
Relevant text chunks fetched
4. Generation
LLM generates answer based on context

Training on Your Private Data

We manage the entire Vector Database pipeline for you. This process, called Retrieval-Augmented Generation (RAG), ensures your chatbot delivers accurate answers based on your private documents, eliminating hallucinations.

  • Factual Accuracy: Answers are anchored in your source documents.

  • Instant Training: Content is ready to use immediately after vectorization.

  • Data Security: Your Vector Data is isolated and secure within your tenant.

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