benchmark
Collection of Qdrant benchmarks (by qdrant)
vector-db-benchmark
Framework for benchmarking vector search engines (by qdrant)
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| benchmark | vector-db-benchmark | |
|---|---|---|
| 1 | 6 | |
| 7 | 224 | |
| - | 12.5% | |
| 0.0 | 9.1 | |
| over 1 year ago | 11 days ago | |
| Python | Python | |
| - | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
benchmark
Posts with mentions or reviews of benchmark.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-01-18.
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[N] We just got funded for an open-source project to make Metric Learning practical.
Regarding Milvus. Well, there are a few essential differences between our projects: - Unlike Milvus, we perform filtering during the search in the vector index, which keeps retrieval complexity close to logarithmic - same as in original HNSW. - We can support complex types of filterable payloads like geo-points - it is not a trivial problem to keep the HNSW search graph connected during filtering. We solved it in our custom implementation of the HNSW index. - Unlike Milvus, we perform a query-planning phase to determine an optimal strategy of executing queries with filters - Qdrant uses Rust programming language - it gives us an advantage in avoiding stop-the-world issues of languages with garbage collection. We also have a retrieval speed benchmark - https://github.com/qdrant/benchmark.
vector-db-benchmark
Posts with mentions or reviews of vector-db-benchmark.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-06-30.
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RAG is Dead. Long Live RAG!
Qdrant’s benchmark results are strongly in favor of accuracy and efficiency. We recommend that you consider them before deciding that an LLM is enough. Take a look at our open-source benchmark reports and try out the tests yourself.
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Evaluate Vector Database / Benchmarks?
Qdrant made their own benchmark. It is quite simple and also takes into consideration more options, so it should be better suited for benchmarking for production purposes.
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Qdrant, Pinecone, Supabase
is noWhen it comes to Supabase, it's using pgvector under the hood, so it would make sense to benchmark it with the other Open Source tools. There is an open PR for that, but it's pretty old: https://github.com/qdrant/vector-db-benchmark/pull/50
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Building a Vector Database with Rust to Make Use of Vector Embeddings
P.S.: Perhaps you want to add your database to our benchmarks repo?
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New and Improved Embedding Model for OpenAI
Do we have any idea why lucene vector search underperforms? As of lucene 9.1 (and elastic 8.4), it runs the same sort of filtered/categorical HNSW that qdrant runs (https://lucene.apache.org/core/9_1_0/core/org/apache/lucene/...). Qdrant's benchmarking code (https://github.com/qdrant/vector-db-benchmark/blob/9263ba/en...) does use the new filtered ann query with elastic 8.4, so it appears to be a fair benchmark. Why is lucene/elastic so much slower? Is it a rust vs. java thing? Or some memory management issues?
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Which vector search engine is the fastest?
There is also an open-source framework for benchmarking https://github.com/qdrant/vector-db-benchmark
What are some alternatives?
When comparing benchmark and vector-db-benchmark you can also consider the following projects:
fashion-mnist - A MNIST-like fashion product database. Benchmark :point_down:
citrus - (distributed) vector database
towhee - Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
ann-benchmarks - Benchmarks of approximate nearest neighbor libraries in Python
vector-search - The definitive guide to using Vector Search to solve your semantic search production workload needs.
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
weaviate-examples - Weaviate vector database – examples
instant-distance - Fast approximate nearest neighbor searching in Rust, based on HNSW index
hora - 🚀 efficient approximate nearest neighbor search algorithm collections library written in Rust 🦀 .