pgvecto.rs
qdrant
pgvecto.rs | qdrant | |
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17 | 142 | |
1,429 | 18,129 | |
14.3% | 4.3% | |
9.3 | 9.9 | |
1 day ago | 3 days ago | |
Rust | Rust | |
Apache License 2.0 | Apache License 2.0 |
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.
pgvecto.rs
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My binary vector search is better than your FP32 vectors
To evaluate the performance metrics in comparison to the original vector approach, we conducted benchmarking using the dbpedia-entities-openai3-text-embedding-3-large-3072-1M dataset. The benchmark was performed on a Google Cloud virtual machine (VM) with specifications of n2-standard-8, which includes 8 virtual CPUs and 32GB of memory. We used pgvecto.rs v0.2.1 as the vector database.
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pgvecto.rs 0.2: Unifying Relational Queries and Vector Search in PostgreSQL
Please check out our documentation for more details. We encourage you to try out pgvecto.rs, benchmark it against your workloads, and contribute your indexing innovations. Join our Discord community to connect with the developers and other users working to improve pgvecto.rs!
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pgvecto.rs alternatives - qdrant and Weaviate
3 projects | 13 Mar 2024
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Milvus VS pgvecto.rs - a user suggested alternative
2 projects | 13 Mar 2024
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You Shouldn't Invest in Vector Databases?
It's kind of a tradeoff. Performance is just one factor when choosing the vector database. In pgvecto.rs https://github.com/tensorchord/pgvecto.rs, we store the index separately from PostgreSQL's internal storage, unlike pgvector's approach. This enable us to get multi-threaded indexing, async indexing without blocking the insertion, and faster search speed comparing to pgvector.
I don't see any fundamental reason why the index in Postgres would be slower than a specialized vector database. The query pattern of the vector database is simply a point query using an index, similar to other queries in an OLTP system.
The only limitation I see is scalability. It's not easy to make PostgreSQL distributed, but solutions like Citus exist, making it still possible.
(I'm the author of pgvecto.rs)
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How We Made PostgreSQL a Better Vector Database
Hi, we've solved the problem you mentioned! Please take a look on our open source postgres vector extension https://github.com/tensorchord/pgvecto.rs.
Our index building process is significantly faster than pgvector on hnsw because we can utilize all the cores, whereas pgvector can only use one core. And for the filter support, we do support pre-filtering, which will guarantee enough results no matter the condition is.
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First Postgres Vector Extension with Filtering Support
Hi,
In our previous post titled “Do we really need a specialized vector database?” on HN (https://news.ycombinator.com/item?id=37097004) we discussed the importance of using a Postgres-based solution for vector search. However, we acknowledged that existing Postgres vector extensions lack support for metadata filtering.
We are excited to announce that we have now addressed this limitation. We are proud to be the first (https://github.com/tensorchord/pgvecto.rs) to enable conditional filtering directly on HNSW indexes within Postgres. This breakthrough allows for efficient and effective metadata filtering in combination with vector search, eliminating the tradeoff previously associated with using Postgres for this purpose.
We invite you to explore our updated offering and experience the benefits of seamless metadata filtering within a Postgres-based vector search system.
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A Summary of LLMOps
Yeah, I think in many cases you just need a vector search lib, instead of a DB.
And in some other cases, you may want postgres vector extension e.g. https://github.com/tensorchord/pgvecto.rs instead of a specialized vector db.
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An early look at HNSW performance with pgvector
Seems that pgvector has a viable competitor extension: https://github.com/tensorchord/pgvecto.rs
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20x Faster as the Beginning: Introducing pgvecto.rs extension written in Rust
We are thrilled to announce the release of https://github.com/tensorchord/pgvecto.rs, a powerful Postgres extension for vector similarity search written in Rust. Its HNSW algorithm is 20x faster than pgvector at 90% recall. But speed is just the start - pgvecto.rs is architected to add new algorithms easily. We've made it an extensible architecture for contributors to implement the new indexes quickly, and we look forward to the open-source community driving pgvecto.rs to new heights!
qdrant
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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)
What are some alternatives?
pgvector - Open-source vector similarity search for Postgres
Milvus - A cloud-native vector database, storage for next generation AI applications
modelz-llm - OpenAI compatible API for LLMs and embeddings (LLaMA, Vicuna, ChatGLM and many others)
Weaviate - Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
pgvecto.rs-bench
faiss - A library for efficient similarity search and clustering of dense vectors.
Awesome-LLMOps - An awesome & curated list of best LLMOps tools for developers
faiss-rs - Rust language bindings for Faiss
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
DocumentGPT - DocumentGPT is a web application that allows you to chat over your research document using OpenAI's chat API and perform semantic search using vector databases. This tool provides a seamless interface for interacting with your research document, exploring search results, and engaging in a conversation with an AI chatbot.
towhee - Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.