Retrieval
How does an embedding index differ from a vector database?
A vector database stores vectors and answers similarity queries. An embedding index is that search capability declared on the same table that holds the source pieces.
Updated · Part of What is an embedding index?
How it works
- The vector database is a product whose rows are vectors plus metadata.
- The embedding index is a structure on a table that also stores the passage, frame, or file.
- You pick a separate vector database when something else owns the source of truth. You declare an index when the table already does.
What it is not
Replacing a vector database does not, by itself, give you RAG. Chunking, staying in sync, and calling the model are still separate jobs.
embedding index versus a vector database: this, and the thing it is confused with
| This | Not this | |
|---|---|---|
| Source of truth | The table, if the index is on it | The vector database, if you copied vectors in |
| Sync | The row update | An ETL job |
| RAG | Still needs chunks and a model call | Not finished when the index exists |
Where Pixeltable fits
Pixeltable keeps the index on the table. Pinecone and LanceDB remain options when a separate vector store is the product you want. They are not required for the index itself.
Questions
How does embedding index versus a vector database work?
The vector database is a product whose rows are vectors plus metadata. The embedding index is a structure on a table that also stores the passage, frame, or file. You pick a separate vector database when something else owns the source of truth. You declare an index when the table already does.
What is embedding index versus a vector database often confused with?
Replacing a vector database does not, by itself, give you RAG. Chunking, staying in sync, and calling the model are still separate jobs.
Next step
Install the Pixeltable skill, or sign up for Cloud and run the same file on a hosted database.