How to properly re-embed a vector database? or provide document context to AI?

Hi @dan

Thank you for your question.

To update or re-embed a vector database for your AI-assisted document editor, start by tracking changes in your document through a version control system or change log. Update vectors either incrementally by recalculating embeddings for modified sections or by re-embedding the entire document if changes are frequent. Remove outdated vectors from the database and insert the updated ones to maintain consistency with the current document state. Alternatively, use a sliding context window to manage relevance without frequent re-embedding, or store document versions and segments in a database for efficient retrieval. Combining vector embeddings with metadata storage can also be effective, using a vector database for similarity searches and a metadata store for document tracking. Ensure that your vector database is scalable and maintains alignment between document content and vector representations. Tools like Azure AI Search can manage and query vector data, while Azure OpenAI Service can integrate AI capabilities. Configure your Azure AI Search index with appropriate vector fields and profiles to ensure proper integration. By implementing these strategies, you can keep your AI chat contextually relevant and responsive to the latest document updates.

Please refer set-up-embedding-models, to see if it helps in your use case. Thank you.