Hendler/flame

Let's light this candle!

Rust

2

15 commits

updated Oct 30, 2024

See the code

README

FLAME

Currently this is a proof of concept Postgresql plugin for text embeddings. Goal is to use Flame with PGVector for faster RAG applications in Postgresql. Model chosen is MiniLM-L6-v2, since I was using it already with 768 length.

The proof of concept (v0.0.1) uses PGRX and fastembed-rs Rust implementation of fastembed

I don't know Rust, or Candle, but would love to see more embeddings... embedded.

Flame is named for its ambition to use HuggingFace's Candle library.

usage

see SETUP.md

cargo pgrx run
# [everything just works...]
flame=# CREATE EXTENSION flame;
flame=# SELECT fast_embed('hello world');

Release Notes

v0.0.1

  • singleton for instatiation
  • default model

Roadmap

v0.1.0

  • tests (long text strings, peformance, multi threading situations, with pgvector)
  • choose models at runtime

v0.2.0

  • auto create pgvector
  • more tests

Contributors

Hendler

15 commits

Hendler/flame

Let's light this candle!

Rust

2

15 commits

updated Oct 30, 2024

See the code

README

FLAME

Currently this is a proof of concept Postgresql plugin for text embeddings. Goal is to use Flame with PGVector for faster RAG applications in Postgresql. Model chosen is MiniLM-L6-v2, since I was using it already with 768 length.

The proof of concept (v0.0.1) uses PGRX and fastembed-rs Rust implementation of fastembed

I don't know Rust, or Candle, but would love to see more embeddings... embedded.

Flame is named for its ambition to use HuggingFace's Candle library.

usage

see SETUP.md

cargo pgrx run
# [everything just works...]
flame=# CREATE EXTENSION flame;
flame=# SELECT fast_embed('hello world');

Release Notes

v0.0.1

  • singleton for instatiation
  • default model

Roadmap

v0.1.0

  • tests (long text strings, peformance, multi threading situations, with pgvector)
  • choose models at runtime

v0.2.0

  • auto create pgvector
  • more tests

Contributors

Hendler

15 commits

Languages

Rust

100.0%