instant-distance
ann-benchmarks
instant-distance | ann-benchmarks | |
---|---|---|
7 | 51 | |
281 | 4,604 | |
0.4% | - | |
5.6 | 7.7 | |
about 1 month ago | 4 days ago | |
Rust | Python | |
Apache License 2.0 | MIT License |
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instant-distance
- Show HN: A fast HNSW implementation in Rust
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Hierarchical Navigable Small Worlds
https://github.com/instant-labs/instant-distance is a compact, fairly readable, pretty fast implementation of the paper in Rust.
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Building a Vector Database with Rust to Make Use of Vector Embeddings
When I looked at it the Rust-CV HNSW implementation was pretty messy, and it looks like it hasn't seen any commits in 2 years. This is partly why we started instant-distance as an alternative, which I think has come out pretty well (for the particular use cases that it serves).
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DiskANN Pure Rust Implementation Interest
I believe u/dochtman's implementation of HNSW is about as good as HNSW is going to get. Competing with the scalability and features (like streamed updates) of FAISS is what I hope to accomplish with this project. Based on interest, I'm now leaning towards an MIT license for the implementation.
- Approaches to looking up data in 2d space
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Hierarchical Navigable Small Worlds (HNSW)
I wrote an HNSW implementation in pure Rust:
https://github.com/InstantDomain/instant-distance
It works pretty well for us at InstantDomainSearch.
I like to think that this is a fairly idiomatic Rust implementation so it might be easier to follow than Facebook's FAISS. It's kinda similar in design to FAISS, so I think it might achieve similar performance, though we haven't spent enough time benchmarking yet.
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Using Aligned Word Vectors for Instant Translations with Python and Rust
We've released the underlying Rust implementation here: https://github.com/InstantDomain/instant-distance with Python bindings at https://pypi.org/project/instant-distance — feedback welcome!
ann-benchmarks
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Using Your Vector Database as a JSON (Or Relational) Datastore
On top of my head, pgvector only supports 2 indexes, those are running in memory only. They don't support GPU indexing, nor Disk based indexing, they also don't have separation of query and insertions.
Also with different people I've talked to, they struggle with scale past 100K-1M vector.
You can also have a look yourself from a performance perspective: https://ann-benchmarks.com/
- ANN Benchmarks
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Approximate Nearest Neighbors Oh Yeah
https://ann-benchmarks.com/ is a good resource covering those libraries and much more.
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pgvector vs Pinecone: cost and performance
We utilized the ANN Benchmarks methodology, a standard for benchmarking vector databases. Our tests used the dbpedia dataset of 1,000,000 OpenAI embeddings (1536 dimensions) and inner product distance metric for both Pinecone and pgvector.
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Vector database is not a separate database category
Data warehouses are columnar stores. They are very different from row-oriented databases - like Postgres, MySQL. Operations on columns - e.g., aggregations (mean of a column) are very efficient.
Most vector databases use one of a few different vector indexing libraries - FAISS, hnswlib, and scann (google only) are popular. The newer vector dbs, like weaviate, have introduced their own indexes, but i haven't seen any performance difference -
Reference: https://ann-benchmarks.com/
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How We Made PostgreSQL a Better Vector Database
(Blog author here). Thanks for the question. In this case the index for both DiskANN and pgvector HNSW is small enough to fit in memory on the machine (8GB RAM), so there's no need to touch the SSD. We plan to test on a config where the index size is larger than memory (we couldn't this time due to limitations in ANN benchmarks [0], the tool we use).
To your question about RAM usage, we provide a graph of index size. When enabling PQ, our new index is 10x smaller than pgvector HNSW. We don't have numbers for HNSWPQ in FAISS yet.
[0]: https://github.com/erikbern/ann-benchmarks/
- Do we think about vector dbs wrong?
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Vector Search with OpenAI Embeddings: Lucene Is All You Need
In terms of "All You Need" for Vector Search, ANN Benchmarks (https://ann-benchmarks.com/) is a good site to review when deciding what you need. As with anything complex, there often isn't a universal solution.
txtai (https://github.com/neuml/txtai) can build indexes with Faiss, Hnswlib and Annoy. All 3 libraries have been around at least 4 years and are mature. txtai also supports storing metadata in SQLite, DuckDB and the next release will support any JSON-capable database supported by SQLAlchemy (Postgres, MariaDB/MySQL, etc).
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Vector databases: analyzing the trade-offs
pg_vector doesn't perform well compared to other methods, at least according to ANN-Benchmarks (https://ann-benchmarks.com/).
txtai is more than just a vector database. It also has a built-in graph component for topic modeling that utilizes the vector index to autogenerate relationships. It can store metadata in SQLite/DuckDB with support for other databases coming. It has support for running LLM prompts right with the data, similar to a stored procedure, through workflows. And it has built-in support for vectorizing data into vectors.
For vector databases that simply store vectors, I agree that it's nothing more than just a different index type.
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Vector Dataset benchmark with 1536/768 dim data
The reason https://ann-benchmarks.com is so good, is that we can see a plot of recall vs latency. I can see you have some latency numbers in the leaderboard at the bottom, but it's very difficult to make a decision.
As a practitioner that works with vector databases every day, just latency is meaningless to me, because I need to know if it's fast AND accurate, and what the tradeoff is! You can't have it both ways. So it would be helpful if you showed plots showing this tradeoff, similar to ann-benchmarks.