VectorChord Engineering Blog & Releases
Vector Search at 10,000 QPS in PostgreSQL with VectorChord
VectorChord, the successor to pgvecto.rs, is a PostgreSQL extension for vector search, scaling up to 10,000 QPS with ease.
VectorChord-BM25: Revolutionize PostgreSQL Search with BM25 Ranking — 3x Faster Than ElasticSearch
VectorChord-BM25 enhances PostgreSQL search with BM25 ranking, 3x faster than ElasticSearch. Supports BERT, Tocken, and Unicode tokenizers
Do we really need a specialized vector database? (2023)
Notice: This article, originally published in 2023, discusses the relevance and necessity of specialized vector databases, particularly in relation to…
VectorChord 0.2: 60K-Dimensional Vectors, 50% Faster Indexing with ARM
VectorChord 0.2: Enhanced vector search in PostgreSQL with up to 60K dimensions, Float16 support, and faster ARM performance
Supercharge vector search with ColBERT rerank in PostgreSQL
Learn to enhance vector search accuracy with ColBERT rerank using PostgreSQL and VectorChord in this comprehensive tutorial
ARM + VectorChord: Affordable, Fast Vector Search
ARM64 servers like AWS Graviton save costs. VectorChord - 26x cheaper than pgvector. Optimized for ARM64/x86, it delivers fast, efficient vector search
Why HNSW is Not the Answer
HNSW (Hierarchical Navigable Small World) has become the go-to algorithm for many vector databases. Its multi-layered graph structure and ability to…
Benefits and Steps of External Centroids Building in VectorChord
Enhance vector search efficiency with external centroids building in VectorChord using external resources and hierarchical K-means
VectorChord: Store 400k Vectors for $1 in PostgreSQL
Introducing VectorChord: The successor to pgvecto.rs. Using VectorChord, you can achieve a QPS of 131 with 0.95 precision on 100 million 768D dataset
Introducing PGVecto.rs Cloud - Scalable Vector Search in PostgreSQL, Now in the Cloud
In an exciting development, we are thrilled to announce the launch of PGVecto.rs Cloud - bringing the power of scalable vector search to your PostgreSQL…
Introducing Terraform Support for PGVecto.rs Cloud
Use Terraform to manage PGVecto.rs Cloud like your other infrastructure components
Unleash the power of sparse vector
Unlock the potential of data pipelines with pure vector search using sparse vectors, BGE-M3, and pgvecto.rs
pg_bestmatch.rs: Elevate Your PostgreSQL Text Queries with BM25
Postgres BM25 full-text search, better RAG, hybrid search
My binary vector search is better than your FP32 vectors
Binary Vector Search: The 30x Memory Reduction Revolution with Preserved Accuracy
pgvector vs. pgvecto.rs in 2024: A Comprehensive Comparison for Vector Search in PostgreSQL
In this blog post, we will illustrate the differences between pgvector and pgvecto.rs.
Do we really need a specialized vector database?
With the popularity of LLM (Large Language Model), vector databases have also become a hot topic. With just a few lines of simple Python code, a vector…
Building the RAG pipeline with LlamaIndex and pgvecto.rs
In this blog post, we will show you how to build a RAG pipeline with pgvecto.rs and LlamaIndex.
Building LLM Applications in LangChain using pgvecto.rs
Supercharge your LLM (Large Language Model) applications in LangChain by leveraging the powerful capabilities of pgvecto.rs
Vector Search at 10,000 QPS in PostgreSQL with VectorChord
VectorChord, the successor to pgvecto.rs, is a PostgreSQL extension for vector search, scaling up to 10,000 QPS with ease.
VectorChord-BM25: Revolutionize PostgreSQL Search with BM25 Ranking — 3x Faster Than ElasticSearch
VectorChord-BM25 enhances PostgreSQL search with BM25 ranking, 3x faster than ElasticSearch. Supports BERT, Tocken, and Unicode tokenizers
Do we really need a specialized vector database? (2023)
Notice: This article, originally published in 2023, discusses the relevance and necessity of specialized vector databases, particularly in relation to…
VectorChord 0.2: 60K-Dimensional Vectors, 50% Faster Indexing with ARM
VectorChord 0.2: Enhanced vector search in PostgreSQL with up to 60K dimensions, Float16 support, and faster ARM performance
Supercharge vector search with ColBERT rerank in PostgreSQL
Learn to enhance vector search accuracy with ColBERT rerank using PostgreSQL and VectorChord in this comprehensive tutorial
ARM + VectorChord: Affordable, Fast Vector Search
ARM64 servers like AWS Graviton save costs. VectorChord - 26x cheaper than pgvector. Optimized for ARM64/x86, it delivers fast, efficient vector search
Why HNSW is Not the Answer
HNSW (Hierarchical Navigable Small World) has become the go-to algorithm for many vector databases. Its multi-layered graph structure and ability to…
Benefits and Steps of External Centroids Building in VectorChord
Enhance vector search efficiency with external centroids building in VectorChord using external resources and hierarchical K-means
VectorChord: Store 400k Vectors for $1 in PostgreSQL
Introducing VectorChord: The successor to pgvecto.rs. Using VectorChord, you can achieve a QPS of 131 with 0.95 precision on 100 million 768D dataset
Introducing PGVecto.rs Cloud - Scalable Vector Search in PostgreSQL, Now in the Cloud
In an exciting development, we are thrilled to announce the launch of PGVecto.rs Cloud - bringing the power of scalable vector search to your PostgreSQL…
Introducing Terraform Support for PGVecto.rs Cloud
Use Terraform to manage PGVecto.rs Cloud like your other infrastructure components
Unleash the power of sparse vector
Unlock the potential of data pipelines with pure vector search using sparse vectors, BGE-M3, and pgvecto.rs
pg_bestmatch.rs: Elevate Your PostgreSQL Text Queries with BM25
Postgres BM25 full-text search, better RAG, hybrid search
My binary vector search is better than your FP32 vectors
Binary Vector Search: The 30x Memory Reduction Revolution with Preserved Accuracy
pgvector vs. pgvecto.rs in 2024: A Comprehensive Comparison for Vector Search in PostgreSQL
In this blog post, we will illustrate the differences between pgvector and pgvecto.rs.
Do we really need a specialized vector database?
With the popularity of LLM (Large Language Model), vector databases have also become a hot topic. With just a few lines of simple Python code, a vector…
Building the RAG pipeline with LlamaIndex and pgvecto.rs
In this blog post, we will show you how to build a RAG pipeline with pgvecto.rs and LlamaIndex.
Building LLM Applications in LangChain using pgvecto.rs
Supercharge your LLM (Large Language Model) applications in LangChain by leveraging the powerful capabilities of pgvecto.rs
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Vector Search at 10,000 QPS in PostgreSQL with VectorChord
VectorChord is a PostgreSQL extension designed for scalable, high-performance, and disk-efficient vector similarity search, and serves as the successor to pgvecto.rs. In our previous blog post, we showed that with just $250 per month, VectorChord …
VectorChord is a PostgreSQL extension designed for scalable, high-performance, and disk-efficient vector similarity search, and serves as the successor to pgvecto.rs. In our previous blog post, we showed that with just $250 per month, VectorChord …
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VectorChord-BM25: Revolutionize PostgreSQL Search with BM25 Ranking — 3x Faster Than ElasticSearch
We’re excited to share something special with you: VectorChord-BM25, a new extension designed to make PostgreSQL’s full-text search even better. Whether you’re building a small app or managing a large-scale system, this tool brings advanced BM25 …
We’re excited to share something special with you: VectorChord-BM25, a new extension designed to make PostgreSQL’s full-text search even better. Whether you’re building a small app or managing a large-scale system, this tool brings advanced BM25 …
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Do we really need a specialized vector database? (2023)
Notice: This article, originally published in 2023, discusses the relevance and necessity of specialized vector databases, particularly in relation to Postgres and its capabilities. For further insights and discussions on this topic, please refer to …
Notice: This article, originally published in 2023, discusses the relevance and necessity of specialized vector databases, particularly in relation to Postgres and its capabilities. For further insights and discussions on this topic, please refer to …
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VectorChord 0.2: 60K-Dimensional Vectors, 50% Faster Indexing with ARM
Today, we’re thrilled to share VectorChord 0.2—a release shaped by community feedback and designed to tackle the evolving challenges of vector search in PostgreSQL. %[https://github.com/tensorchord/VectorChord/] Optimized Storage Layout: Supporting …
Today, we’re thrilled to share VectorChord 0.2—a release shaped by community feedback and designed to tackle the evolving challenges of vector search in PostgreSQL. %[https://github.com/tensorchord/VectorChord/] Optimized Storage Layout: Supporting …
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Supercharge vector search with ColBERT rerank in PostgreSQL
Traditional vector search methods typically employ sentence embeddings to locate similar content. However, generating sentence embeddings through pooling token embeddings can potentially sacrifice fine-grained details present at the token level. …
Traditional vector search methods typically employ sentence embeddings to locate similar content. However, generating sentence embeddings through pooling token embeddings can potentially sacrifice fine-grained details present at the token level. …
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ARM + VectorChord: Affordable, Fast Vector Search
ARM64 is quietly becoming a go-to for cost-effective cloud servers, especially on AWS. With ARM-based processors like the Graviton series (Graviton3 and Graviton4), you can save a good chunk of money without sacrificing performance. These ARM64 …
ARM64 is quietly becoming a go-to for cost-effective cloud servers, especially on AWS. With ARM-based processors like the Graviton series (Graviton3 and Graviton4), you can save a good chunk of money without sacrificing performance. These ARM64 …
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Why HNSW is Not the Answer
HNSW (Hierarchical Navigable Small World) has become the go-to algorithm for many vector databases. Its multi-layered graph structure and ability to efficiently navigate vector embeddings make it particularly appealing. However, despite its apparent …
HNSW (Hierarchical Navigable Small World) has become the go-to algorithm for many vector databases. Its multi-layered graph structure and ability to efficiently navigate vector embeddings make it particularly appealing. However, despite its apparent …
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Benefits and Steps of External Centroids Building in VectorChord
Clustering is an important component in vector search, and the k-means algorithm remains one of the most widely used techniques. However, its simplicity comes with a significant drawback: resource-intensive computation can render it impractical for …
Clustering is an important component in vector search, and the k-means algorithm remains one of the most widely used techniques. However, its simplicity comes with a significant drawback: resource-intensive computation can render it impractical for …
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VectorChord: Store 400k Vectors for $1 in PostgreSQL
We’re pleased to announce our new vector search extension for PostgreSQL, providing a highly cost-effective way to manage large vectors. Using VectorChord, you can achieve a QPS of 131 with 0.95 precision on 100 million 768-dimensional vectors for …
We’re pleased to announce our new vector search extension for PostgreSQL, providing a highly cost-effective way to manage large vectors. Using VectorChord, you can achieve a QPS of 131 with 0.95 precision on 100 million 768-dimensional vectors for …
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Introducing PGVecto.rs Cloud - Scalable Vector Search in PostgreSQL, Now in the Cloud
In an exciting development, we are thrilled to announce the launch of PGVecto.rs Cloud - bringing the power of scalable vector search to your PostgreSQL databases, now available as a fully managed cloud service. PGVecto.rs is a pioneering Postgres …
In an exciting development, we are thrilled to announce the launch of PGVecto.rs Cloud - bringing the power of scalable vector search to your PostgreSQL databases, now available as a fully managed cloud service. PGVecto.rs is a pioneering Postgres …
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Introducing Terraform Support for PGVecto.rs Cloud
We are thrilled to announce the release of our Terraform provider for PGVecto.rs Cloud. This new provider is designed to simplify the process of managing resources on our cloud service, providing users with an intuitive and streamlined experience for …
We are thrilled to announce the release of our Terraform provider for PGVecto.rs Cloud. This new provider is designed to simplify the process of managing resources on our cloud service, providing users with an intuitive and streamlined experience for …
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Unleash the power of sparse vector
In the past, hybrid search combined two search methods: traditional keyword-based search and vector-based similarity search. However, sparse vector can act as a substitute for keyword search, unlocking the full potential of the data pipeline with …
In the past, hybrid search combined two search methods: traditional keyword-based search and vector-based similarity search. However, sparse vector can act as a substitute for keyword search, unlocking the full potential of the data pipeline with …
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pg_bestmatch.rs: Elevate Your PostgreSQL Text Queries with BM25
Tip Update: Check our latest extension VectorChord-BM25 for end2end BM25 ranking in PostgreSQL. Read more at https://vchord.pgsty.com/blog/post/vectorchord-bm25-revolutionize-postgresql-search-with-bm25-ranking-3x-faster-than-elasticsearch/ …
Tip Update: Check our latest extension VectorChord-BM25 for end2end BM25 ranking in PostgreSQL. Read more at https://vchord.pgsty.com/blog/post/vectorchord-bm25-revolutionize-postgresql-search-with-bm25-ranking-3x-faster-than-elasticsearch/ …
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My binary vector search is better than your FP32 vectors
Within the field of vector search, an intriguing development has arisen: binary vector search. This approach shows promise in tackling the long-standing issue of memory consumption by achieving a remarkable 30x reduction. However, a critical aspect …
Within the field of vector search, an intriguing development has arisen: binary vector search. This approach shows promise in tackling the long-standing issue of memory consumption by achieving a remarkable 30x reduction. However, a critical aspect …
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pgvector vs. pgvecto.rs in 2024: A Comprehensive Comparison for Vector Search in PostgreSQL
pgvector and pgvecto.rs are both vector search extensions designed to enhance the capabilities of PostgreSQL. These extensions revolutionize the way vector search is performed within the database, providing scalable, low-latency, and hybrid-enabled …
pgvector and pgvecto.rs are both vector search extensions designed to enhance the capabilities of PostgreSQL. These extensions revolutionize the way vector search is performed within the database, providing scalable, low-latency, and hybrid-enabled …
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Do we really need a specialized vector database?
With the popularity of LLM (Large Language Model), vector databases have also become a hot topic. With just a few lines of simple Python code, a vector database can act as a cheap but highly effective “external brain” for your LLM. But do we really …
With the popularity of LLM (Large Language Model), vector databases have also become a hot topic. With just a few lines of simple Python code, a vector database can act as a cheap but highly effective “external brain” for your LLM. But do we really …
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Building the RAG pipeline with LlamaIndex and pgvecto.rs
LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models (LLMs). pgvecto.rs provides a LlamaIndex integration that allows you to retrieve the most similar vectors. In this blog post, we will show you …
LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models (LLMs). pgvecto.rs provides a LlamaIndex integration that allows you to retrieve the most similar vectors. In this blog post, we will show you …
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Building LLM Applications in LangChain using pgvecto.rs
LangChain is a framework for developing applications powered by language models. It enables applications that: Are context-aware: connect a language model to sources of context (prompt instructions, few shot examples, content to ground its response …
LangChain is a framework for developing applications powered by language models. It enables applications that: Are context-aware: connect a language model to sources of context (prompt instructions, few shot examples, content to ground its response …