vector-db-benchmark
Framework for benchmarking vector search engines (by qdrant)
qdrant
Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/ (by qdrant)
vector-db-benchmark | qdrant | |
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6 | 141 | |
227 | 17,943 | |
7.9% | 3.4% | |
9.1 | 9.9 | |
5 days ago | 6 days ago | |
Python | Rust | |
Apache License 2.0 | 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.
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
qdrant
Posts with mentions or reviews of qdrant.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-05-02.
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Hindi-Language AI Chatbot for Enterprises Using Qdrant, MLFlow, and LangChain
Great. Now that we have the embeddings, we need to store them in a vector database. We will be using Qdrant for this purpose. Qdrant is an open-source vector database that allows you to store and query high-dimensional vectors. The easiest way to get started with the Qdrant database is using the docker.
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Boost Your Code's Efficiency: Introducing Semantic Cache with Qdrant
I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours.
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Ask HN: Has Anyone Trained a personal LLM using their personal notes?
I'm currently looking to implement locally, using QDrant [1] for instance.
I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2].
[1]. https://qdrant.tech/
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Show HN: A fast HNSW implementation in Rust
Also compare with qdrant's Rust implementation; they tout their performance. https://github.com/qdrant/qdrant/tree/master/lib/segment/src...
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pgvecto.rs alternatives - qdrant and Weaviate
3 projects | 13 Mar 2024
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Open-source Rust-based RAG
There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb
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Qdrant 1.8.0 - Major Performance Enhancements
For more information, see our release notes. Qdrant is an open source project. We welcome your contributions; raise issues, or contribute via pull requests!
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Perform Image-Driven Reverse Image Search on E-Commerce Sites with ImageBind and Qdrant
Initialize the Qdrant Client with in-memory storage. The collection name will be “imagebind_data” and we will be using cosine distance.
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7 Vector Databases Every Developer Should Know!
Qdrant is an open-source vector search engine optimized for performance and flexibility. It supports both exact and approximate nearest neighbor search, providing a balance between accuracy and speed for various AI and ML applications.
- Ask HN: Who is hiring? (February 2024)