tdyar/iris-vector-ai

To write IRIS vector syntax for AI similarity search, use VECTOR(DOUBLE, 384) for vector columns and AS HNSW(Distance='Cosine') for indices. This Skill enforces strict syntax rules to ensure correct vector writing, embedding, and similarity matching across IRIS versions.

IRIS HNSW indexing requires the AS HNSW(Distance='Cosine') syntax, while embedding functions use EMBEDDING('config-name', ?). This mechanism ensures safe vector operations and proper cosine similarity calculations without relying on pgvector conventions.

You perform vector similarity matching in IRIS by using native vector cosine similarity functions instead of pgvector. This Skill provides hardened syntax rules to avoid unsafe operations and ensure correct embedding retrieval and similarity matching.

IRIS does not use pgvector syntax for vector writing or embedding retrieval. It enforces its own strict syntax, such as VECTOR(DOUBLE, 384) and EMBEDDING('config-name', ?), to prevent confusion and unsafe operations during AI-driven vector tasks.

IRIS vector queries fail during HNSW index creation when syntax deviates from strict requirements like AS HNSW(Distance='Cosine'). This Skill enforces correct function calls and safe patterns to avoid errors across IRIS versions.

Yes, you can use IRIS vector syntax for knowledge base search and embedding retrieval. Engineers use these hardened vector syntax rules to generate proper queries for similarity matching and AI-driven vector tasks safely.