Configure HNSW/IVF indices and perform ANN search with vector embeddings.
npx skills add https://github.com/mahg-es/araya --skill vector-search-mahg-es
Please help me install this Agent Skill. Skill: vector-search Source: https://github.com/mahg-es/araya/tree/main/skills/vector-search Command: npx skills add https://github.com/mahg-es/araya --skill vector-search-mahg-es
Enable semantic retrieval by representing data with vector embeddings and performing ANN search. Vector search finds semantically similar content — even when exact words don't match — powering RAG, recommendation, and similarity applications.
Configure a vector search over your dataset using embeddings and an ANN index to retrieve semantically similar results.
High-intent search queries and answers about installing and using this skill.
Semantic vector search retrieves similar content by representing data with vector embeddings and performing ANN search. It scores semantic similarity between unstructured data points, enabling retrieval even when exact keywords do not match.
You can configure HNSW and IVF indices for embedding-based retrieval by tuning index parameters during your vector search setup. This process optimizes the approximate nearest neighbor search pipeline for efficient querying and similarity scoring.
The best way to build a RAG pipeline with vector embeddings is to implement a complete pipeline from data chunking to embedding, upsert, and query. This pipeline applies index configuration to retrieve semantically similar results.
Yes, you can combine vector similarity scores with text search scores for hybrid search. This approach integrates semantic retrieval with traditional text scores to improve ranking across unstructured data applications.
To select an embedding model and tune ANN index parameters for semantic retrieval, you must configure your vector search pipeline. This involves choosing appropriate embedding representations and adjusting HNSW or IVF settings for optimal similarity search.
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