vector-db-benchmark
weaviate-examples
| vector-db-benchmark | weaviate-examples | |
|---|---|---|
| 6 | 2 | |
| 227 | 283 | |
| 7.9% | 1.4% | |
| 9.1 | 2.7 | |
| 5 days ago | 3 months ago | |
| Python | HTML | |
| 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.
vector-db-benchmark
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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
weaviate-examples
- FLaNK Stack 26 February 2024
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How to build an Image Search Application with Weaviate and Python
Github: https://github.com/semi-technologies/weaviate-examples/tree/main/nearest-neighbor-dog-search
What are some alternatives?
citrus - (distributed) vector database
awesome-vector-database - A curated list of awesome works related to high dimensional structure/vector search & database
ann-benchmarks - Benchmarks of approximate nearest neighbor libraries in Python
goodcode - A curated collection of annotated code examples from prominent open-source projects
vector-search - The definitive guide to using Vector Search to solve your semantic search production workload needs.
code-examples - Short code snippets written by our open source community!
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
deeplake - Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai
instant-distance - Fast approximate nearest neighbor searching in Rust, based on HNSW index
orama - 🌌 Fast, dependency-free, full-text and vector search engine with typo tolerance, filters, facets, stemming, and more. Works with any JavaScript runtime, browser, server, service!
hora - 🚀 efficient approximate nearest neighbor search algorithm collections library written in Rust 🦀 .