4 ms·

Looks really nice, but the only concern I had — how does the perf compare to more mature libraries like faiss?

alexgarcia-xyz 2y ago
Benchmarking for this project is a bit weird, since 1) only linear scans are supported, and 2) it's an "embeddable" vector search tool, so it doesn't make a lot of sense to benchmark against "server" vector databases like qdrant or pinecone. That being said, ~generally~ I'd say it's faster than using numpy and tools like txtai/chromadb. Faiss and hnswlib (bruteforce) are faster because they store everything in memory and use multiple threads. But for smaller vector indexes, I don't think you'd notice much of a difference. sqlite-vec has some support for SIMD operations, which speeds things up quite a bit, but Faiss still takes the cake.
- dmezzetti 2y ago
  Author of txtai here - great work with this extension. I wouldn't consider it a "this or that" decision. While txtai does combine Faiss and SQLite, it could also utilize this extension. The same task was just done for Postgres + pgvector. txtai is not tied to any particular backend components.
  - alexgarcia-xyz 2y ago
    Ya I worded this part awkwardly - I was hinting that querying a vector index and joining with metadata with sqlite + sqlite-vec (in a single SQL join) will probably be faster than other methods, like txtai, which do the joining phase in a higher level like Python. Which isn't a fair comparison, especially since txtai can switch to much faster vector stores, but I think is fair for most embedded use-cases. That being said, txtai offers way more than sqlite-vec, like builtin embedding models and other nice LLM features, so it's definitely apples to oranges.
    - dmezzetti 2y ago
      I'll keep an eye on this extension. With this, what DuckDB just added and pgvector, we're seeing a blurring of the lines. Back in 2021, there wasn't a RDBMS that had native vector support. But native vector integration makes it possible for txtai to just run SQL-driven vector queries...exciting times. I think systems that bet on existing databases eventually catching up (as is txtai's model) vs trying to reinvent the entire database stack will win out.

Benchmarking for this project is a bit weird, since 1) only linear scans are supported, and 2) it's an "embeddable" vector search tool, so it doesn't make a lot of sense to benchmark against "server" vector databases like qdrant or pinecone. That being said, ~generally~ I'd say it's faster than using numpy and tools like txtai/chromadb. Faiss and hnswlib (bruteforce) are faster because they store everything in memory and use multiple threads. But for smaller vector indexes, I don't think you'd notice much of a difference. sqlite-vec has some support for SIMD operations, which speeds things up quite a bit, but Faiss still takes the cake.

dmezzetti 2y ago
Author of txtai here - great work with this extension. I wouldn't consider it a "this or that" decision. While txtai does combine Faiss and SQLite, it could also utilize this extension. The same task was just done for Postgres + pgvector. txtai is not tied to any particular backend components.
- alexgarcia-xyz 2y ago
  Ya I worded this part awkwardly - I was hinting that querying a vector index and joining with metadata with sqlite + sqlite-vec (in a single SQL join) will probably be faster than other methods, like txtai, which do the joining phase in a higher level like Python. Which isn't a fair comparison, especially since txtai can switch to much faster vector stores, but I think is fair for most embedded use-cases. That being said, txtai offers way more than sqlite-vec, like builtin embedding models and other nice LLM features, so it's definitely apples to oranges.
  - dmezzetti 2y ago
    I'll keep an eye on this extension. With this, what DuckDB just added and pgvector, we're seeing a blurring of the lines. Back in 2021, there wasn't a RDBMS that had native vector support. But native vector integration makes it possible for txtai to just run SQL-driven vector queries...exciting times. I think systems that bet on existing databases eventually catching up (as is txtai's model) vs trying to reinvent the entire database stack will win out.