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查看技能说明SharpVector
Build5Nines
Lightweight, In-memory, Semantic Search, Text Vector Database to embed in any .NET Application
OPENAGENTSKILL / DIRECTORY
为下一项任务找到合适的技能。探索适用于 Codex、Claude Code、Cursor 等 Agent 的工具。
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搜索结果: 29
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查看技能说明Build5Nines
Lightweight, In-memory, Semantic Search, Text Vector Database to embed in any .NET Application
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查看技能说明supervc-stack
Scalable, fast, and disk-friendly vector search in Postgres, the successor of pgvecto.rs.
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查看技能说明zilliztech
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查看技能说明qdrant
Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
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查看技能说明RediSearch
A query and indexing engine for Redis, providing secondary indexing, full-text search, vector similarity search and aggregations.
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查看技能说明HelixDB
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查看技能说明infiniflow
The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense vector, sparse vector, tensor (multi-vector), and full-text.
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查看技能说明matrixorigin
AI-native HTAP database with Git-for-Data and built-in vector search, serving as the data and memory backbone for intelligent agents and applications.
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查看技能说明dingodb
A multi-modal vector database that supports upserts and vector queries using unified SQL (MySQL-Compatible) on structured and unstructured data, while meeting the requir…
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查看技能说明milvus-io
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查看技能说明endee-io
Endee.io – A high-performance vector database, designed to handle up to 1B vectors on a single node, delivering significant performance gains through optimized indexing…
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查看技能说明qdrant
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查看技能说明tensorchord
Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database.
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查看技能说明ArcadeData
ArcadeDB Multi-Model Database, one DBMS that supports SQL, Cypher, Gremlin, HTTP/JSON, MongoDB and Redis. ArcadeDB is a conceptual fork of OrientDB, the first Multi-Mode…
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查看技能说明philippgille
Embeddable vector database for Go with Chroma-like interface and zero third-party dependencies. In-memory with optional persistence.
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查看技能说明lightonai
NextPlaid, ColGREP: Multi-vector search, from database to coding agents.