Vector Streaming: The Memory Efficient Indexing for Vector Databases
summary
Rust-based Embed-Anything implements vector streaming by moving embedding work into a separate thread/process and communicating via MPSC channels, keeping only small buffered batches in memory to avoid retaining large float arrays. Works with Candle and ONNX backends and multiple vector DB adapters, compiles to ~15 MB (vs bulky PyTorch) and shows 25% faster end-to-end indexing in benchmarks; demoed a 40 MB local app doing real-time ingestion and queries while indexing, with adjustable buffer size and an open issue on backpressure.
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chapters (11)
- 0:00Introduction
- 1:52Embeddings overview
- 4:21Design requirements
- 8:36Rust’s role
- 10:22Embedding pipeline
- 13:56Naive embedding challenges
- 16:27Vector Streaming solution
- 19:00Performance gains
- 20:27Using Embed Anything
- 21:32Live demo
- 23:20Q&A
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