Implement scalable, low-latency, and hybrid-supported vector search in Postgres.
pgvecto.rs is a vector search engine implemented in the PostgreSQL database, designed to provide scalable, low-latency vector search capabilities while maintaining the efficiency and stability of the database. It supports hybrid search modes, allowing developers to seamlessly switch between traditional SQL queries and vector searches, thereby optimizing the efficiency and flexibility of data retrieval.
This is the machine-readable structured data for this agent. AI systems and search engines use this to understand the agent's capabilities.
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"url": "https://agentsignals.ai/agents/pgvecto-rs",
"applicationCategory": "开发工具",
"operatingSystem": "GitHub",
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"Low Latency Response",
"Fully Integrated with PostgreSQL"
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"datePublished": "2025-12-05T17:14:51.119939+00:00",
"dateModified": "2025-12-19T05:07:02.168096+00:00",
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"text": "Large-scale data retrieval, Recommendation system development, Image and text similarity search"
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"text": "与现有 PostgreSQL 系统兼容, 低延迟性能, 支持混合查询模式"
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"@type": "Question",
"name": "What are the limitations of pgvecto.rs?",
"acceptedAnswer": {
"@type": "Answer",
"text": "可能需要额外的硬件资源, 对非技术用户不友好"
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