What is Pgvector

With the rapid advancement of Artificial Intelligence and Machine Learning, especially the rise of Large Language Models (LLMs), vector retrieval has become an indispensable part of modern data infrastructure. Vectors, the mathematical representations of complex data like text, images, and audio in high-dimensional space, are central to understanding and retrieving unstructured information.

In this context, Pgvector, an extension for PostgreSQL, was created. It transforms PostgreSQL—one of the industry's most robust and reliable relational databases—into a powerful vector database.

What is Pgvector?

Pgvector is an open-source extension for PostgreSQL that provides the functionality to store, index, and efficiently search vector data. This means you can manage traditional structured data (like user records, transaction details) and modern unstructured data (like embedding vectors) within a single, unified system, significantly simplifying data architecture and operational complexity.

Key Advantages of Pgvector

Unified Data Infrastructure

This is Pgvector's most compelling feature. You no longer need to run and maintain a separate relational database and a separate vector database. With Pgvector, you leverage PostgreSQL's robust ACID transaction guarantees, mature backup/recovery mechanisms, rich data types, and query optimizer to manage your vector data.

Powerful Filtering and Combined Queries

Because vector data is stored directly in PostgreSQL tables, you can easily combine vector similarity search with traditional SQL queries. For instance, you can execute a query to find the documents "most similar to a given product description," while simultaneously requiring those documents to be "created within the last 30 days" and "marked as published."

SQL

-- Example: Find the 10 most similar products to a given vector, published within the last 30 daysSELECT
  product_id,
  description,
  vector_column <-> '[...]' AS distance
FROM
  products
WHERE
  is_published = TRUE AND created_at > NOW() - INTERVAL '30 days'ORDER BY
  distance
LIMIT
  10;

Support for Major Distance Metrics

Pgvector supports several distance metrics for calculating vector similarity, meeting the needs of various application scenarios:

• L2 Distance (Euclidean): $a \leftrightarrow b$
• Inner Product: $a \operatorname{<->} b$
• Cosine Similarity: $a \operatorname{<=>} b$

Efficient Indexing Mechanism

To accelerate similarity search on high-dimensional datasets, Pgvector provides the IVFFlat index. IVFFlat is an Approximate Nearest Neighbor (ANN) algorithm that improves query efficiency by dividing the vector space into clusters (lists), achieving fast retrieval with an acceptable trade-off in precision.

Pgvector Use Cases

• RAG (Retrieval-Augmented Generation) Architecture: Provides external knowledge bases for LLMs, enabling more accurate and timely content generation.
• Semantic Search: Replaces traditional keyword search, retrieving based on the meaning of the query and document rather than literal matching.
• Recommendation Systems: Performs similarity matching based on user and item embedding vectors to recommend relevant content or products.
• Image/Audio Content Identification: Retrieves multimedia data similar in content to a given image or audio sample.

Velodb: Next-Generation Vector Database for Extreme Scale and Hybrid Search

Pgvector offers great convenience due to its integration within the PostgreSQL ecosystem. However, for applications that need to handle trillion-scale data volumes and have extreme requirements for performance and scalability, a purpose-built, vector-optimized database like Velodb offers significant advantages.

Velodb, as a high-performance, next-generation vector database, demonstrates superior capabilities in two key areas:

• Superior Vector Scale: Velodb's architecture is fundamentally designed and deeply optimized from the ground up for high concurrency and the storage and retrieval of massive datasets (e.g., billions to trillions of vectors). It typically employs more advanced, efficient distributed indexing and storage strategies, easily handling hyper-scale data growth while maintaining millisecond-level query latency—a crucial requirement for internet services dealing with global users and vast content.
• Advanced Hybrid Search Support: While Pgvector can perform filtering using SQL WHERE clauses, Velodb offers native, deeply optimized hybrid search capabilities. This goes beyond simple vector retrieval plus structured filtering; it supports:
  • Highly efficient combination of Keyword Search (BM25) and Vector Search, allowing users to simultaneously leverage semantic relevance (vectors) and keyword exact matching (BM25). This significantly boosts the accuracy (Recall) of RAG and search results.
  • Re-ranking and more complex query logic to ensure the best possible balance between retrieval performance and result quality.

For most applications relying on PostgreSQL, Pgvector is an excellent starting point. However, if your application anticipates dealing with petabytes of vector data, requires extremely high QPS, or has stringent demands for the accuracy and performance of hybrid search, Velodb will be a more reliable and forward-looking choice.