lucene
ann-benchmarks
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lucene | ann-benchmarks | |
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11 | 51 | |
2,358 | 4,588 | |
4.0% | - | |
9.8 | 8.1 | |
about 12 hours ago | 4 days ago | |
Java | Python | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
lucene
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Building an efficient sparse keyword index in Python
First, a review of the landscape. As said in the introduction, there aren't a ton of good options. Apache Lucene is by far the best traditional search index from a speed, performance and functionality standpoint. It's the base for Elasticsearch/OpenSearch and many other projects. But it requires Java.
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Java Panama Vector API Integrated with Apache Lucene
https://github.com/apache/lucene/issues/10047
2. The Panama Vector API allows CPU's that support it to accelerate vector operations: https://openjdk.org/jeps/438
So this allows fast ANN on Lucene for semantic search!
How did people do this before Lucene supported it? Only through entirely different tools?
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What Is a Vector Database
Are they forking Lucene or somehow getting the Lucene devs to increase that limit? Because this PR has been open for over a year now: https://github.com/apache/lucene/issues/11507
- An alternative to Elasticsearch that runs on a few MBs of RAM
- Lucene 9.4 (optionally) uses Panama's mapped MemorySegments when JDK 19 is detected
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A primer on Roaring bitmaps: what they are and how they work
Lucene's adaptation of Roaring uses the complement idea on a block-wise basis:
https://github.com/apache/lucene/blob/84cae4f27cfd3feb3bb42d...
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How are documents stored in Elasticsearch?
Like someone said, it's in locations as specified in the path.data. Depending on sharing and replication, it could be on more than one host. Elastic uses Apache Lucene to store documents, since it's open source, that rabbit hole will welcome research :-)
- panama/foreign status update
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Amazon Elasticsearch Service Is Now Amazon OpenSearch Service
It is pretty clear to me that Elastic is planning to build their ANN features differently than OpenDistro's k-NN implementation, or other plugins modules that extend Easticsearch in similar ways. They now will build on the Apache Lucene capabilities that were collaboratively built "upstream" by a number of individuals, some that work for Amazon and some that work for Elastic.
From the linked issue, it seemed that they were originally planning to develop this as a proprietary feature of Elasticsearch, without contributing the functionality to Apache Lucene, but then changed direction when the Apache Lucene developers (some of which are currently employed to do such work by Amazon) started to build its approximate nearest neighbor (ANN) vector search capabilities. [1]
It's great to see folks that work for Elastic collaborating and building on what is in Apache Lucene to extend the utility of ANN with Hierarchical Navigable Small World Graphs (HNSW) [2]! From this, I think it should be possible to implement an Open Source version of the functionality with a compatible API, if that is something that OpenSearch users seek.
[1] https://issues.apache.org/jira/browse/LUCENE-9004
[2] https://github.com/apache/lucene/pull/250
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.
What are some alternatives?
pisa - PISA: Performant Indexes and Search for Academia
pgvector - Open-source vector similarity search for Postgres
Typesense - Open Source alternative to Algolia + Pinecone and an Easier-to-Use alternative to ElasticSearch ⚡ 🔍 ✨ Fast, typo tolerant, in-memory fuzzy Search Engine for building delightful search experiences
faiss - A library for efficient similarity search and clustering of dense vectors.
RoaringBitmap - A better compressed bitset in Java: used by Apache Spark, Netflix Atlas, Apache Pinot, Tablesaw, and many others
Milvus - A cloud-native vector database, storage for next generation AI applications
OpenSearch - 🔎 Open source distributed and RESTful search engine.
tlsh
Apache Solr - Apache Lucene and Solr open-source search software
vald - Vald. A Highly Scalable Distributed Vector Search Engine
resin - Vector space search engine. Available as a HTTP service or as an embedded library.
pgANN - Fast Approximate Nearest Neighbor (ANN) searches with a PostgreSQL database.