databricks-vector-search

To create a vector search index in Databricks, you can set up Delta Sync or Direct Access indexes. This process requires a configured vector search endpoint and Delta Lake integration to enable efficient similarity matching for your RAG application.

Standard and Storage-Optimized endpoints dictate how your vector search indexes are managed in Databricks. You must create and configure these endpoints to handle querying and storage based on your specific scalability and performance requirements.

Yes, you can apply filters to semantic search queries in Databricks. The functionality offers advanced querying capabilities, allowing you to execute complex hybrid searches that combine similarity matching with specific metadata filters.

Yes, the Databricks SDK is a required dependency. You need it along with Delta Lake integration to programmatically manage vector search endpoints, create indexes, and execute similarity matching queries within your environment.

The best way to build a semantic search engine in Databricks is by indexing your documents into a Delta Sync index. You can then query this index using advanced search modes to retrieve highly relevant similarity matches from your knowledge base.