Vector Database Setup — Index Design and Initial Data Ingestion

I will set up and configure your vector database — covering index or collection creation, namespace or schema design for your data structure, metadata schema for filtered retrieval, embedding model integration, and batch ingestion of your initial document set with progress monitoring and validation. A vector store configured without a metadata filtering strategy is significantly less useful in production than one where retrieval can be narrowed by date, category, or other attributes — metadata schema design at setup time determines retrieval precision for the lifetime of the system and is far cheaper to get right initially than to retrofit after documents are indexed.

The setup covers index configuration, embedding model integration, metadata schema design, batch ingestion pipeline, duplicate detection, retrieval validation testing, and a data ingestion guide for ongoing document additions.