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.