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Vector-db-benchmark Alternatives
Similar projects and alternatives to vector-db-benchmark
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qdrant
Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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vector-search
The definitive guide to using Vector Search to solve your semantic search production workload needs.
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hora
🚀 efficient approximate nearest neighbor search algorithm collections library written in Rust 🦀 .
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better vector-db-benchmark alternative or higher similarity.
vector-db-benchmark reviews and mentions
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
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A note from our sponsor - InfluxDB
www.influxdata.com | 25 Apr 2024
Stats
Basic vector-db-benchmark repo stats
6
224
9.1
7 days ago
qdrant/vector-db-benchmark is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of vector-db-benchmark is Python.
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