1 ACCEPTED SOLUTION
Accepted Solutions
Option 1: Delta Sync Index with embeddings computed by Databricks You provide a source Delta table that contains data in text format. Databricks calculates the embeddings, using a model that you specify, and optionally saves the embeddings to a table in Unity Catalog. As the Delta table is updated, the index stays synced with the Delta table.
The following diagram illustrates the process:
Calculate query embeddings. Query can include metadata filters.
Perform similarity search to identify most relevant documents.
Return the most relevant documents and append them to the query.
"Mosaic AI Vector Search is a vector database that is built into the Databricks Data Intelligence Platform and integrated with its governance and productivity tools. A vector database is a database that is optimized to store and retrieve embeddings. Embeddings are mathematical representations of the semantic content of data, typically text or image data. Embeddings are generated by a large language model and are a key component of many GenAI applications that depend on finding documents or images that are similar to each other. Examples are RAG systems, recommender systems, and image and video recognition.
With Mosaic AI Vector Search, you create a vector search index from a Delta table. The index includes embedded data with metadata. You can then query the index using a REST API to identify the most similar vectors and return the associated documents. You can structure the index to automatically sync when the underlying Delta table is updated."
Have you read these docs?
https://docs.databricks.com/en/generative-ai/vector-search.html
https://docs.databricks.com/en/generative-ai/create-query-vector-search.html
Option 1: Delta Sync Index with embeddings computed by Databricks You provide a source Delta table that contains data in text format. Databricks calculates the embeddings, using a model that you specify, and optionally saves the embeddings to a table in Unity Catalog. As the Delta table is updated, the index stays synced with the Delta table.
The following diagram illustrates the process:
Calculate query embeddings. Query can include metadata filters.
Perform similarity search to identify most relevant documents.
Return the most relevant documents and append them to the query.
"Mosaic AI Vector Search is a vector database that is built into the Databricks Data Intelligence Platform and integrated with its governance and productivity tools. A vector database is a database that is optimized to store and retrieve embeddings. Embeddings are mathematical representations of the semantic content of data, typically text or image data. Embeddings are generated by a large language model and are a key component of many GenAI applications that depend on finding documents or images that are similar to each other. Examples are RAG systems, recommender systems, and image and video recognition.
With Mosaic AI Vector Search, you create a vector search index from a Delta table. The index includes embedded data with metadata. You can then query the index using a REST API to identify the most similar vectors and return the associated documents. You can structure the index to automatically sync when the underlying Delta table is updated."
Have you read these docs?
https://docs.databricks.com/en/generative-ai/vector-search.html
https://docs.databricks.com/en/generative-ai/create-query-vector-search.html
