Superlinked

What is Superlinked?

Superlinked is a open source (apache 2.0) ai skills developed by Superlinked Inc.. It is an open-source framework that embeds multi-attribute data—including text, numbers, dates, and categories—into unified vector representations for semantic search and recommendation systems.

Core Technical Capabilities

• Multi-Attribute Vector Spaces: Combines dense semantic text embeddings with structured numerical weights (prices, recency, ratings) in a single mathematical space.
• Dynamic Query Weighting: Allows agents to adjust the relative importance of text similarity versus numerical criteria at search time without re-indexing.
• Vector Database Compatibility: Exports standardized vector indexes to Qdrant, Pinecone, Redis, and Milvus with zero vendor lock-in.

Practical Workflows & Use Cases

• E-Commerce Product Discovery: Ranks products simultaneously by semantic description match, available inventory, customer rating, and price range.
• Financial & Legal Search: Combines document text relevance with strict publication date recency and regulatory classification filters.
• Personalized Recommendation Agents: Embeds user browsing habits and demographic preferences directly into retrieval vector queries.

Superlinked vs Alternatives

When selecting tools in the ai skills ecosystem, developers frequently compare Superlinked against alternative solutions:

• Standard Vector DB Metadata Filtering: Metadata filtering operates as a hard post-filter, while Superlinked embeds numerical constraints directly into the similarity geometry.
• Traditional BM25 Hybrid Search: BM25 handles exact keyword matching, whereas Superlinked handles continuous numerical distributions and reciprocal rank fusion.

Technical Specifications

Frequently Asked Questions