similarity-search-patterns

To implement similarity search for RAG with Pinecone, use this Skill's code templates to store and query document embeddings efficiently. It provides integration patterns for connecting to Pinecone, ingesting vector data, and retrieving semantically similar results.

The best way to optimize vector search latency is by selecting appropriate distance metrics and index types. This Skill provides patterns for Flat, HNSW, and IVF+PQ indexing to ensure optimal performance and scalability for large-scale vector datasets.

Yes, this Skill supports pgvector and Weaviate for semantic search. It includes vector database integration patterns for Pinecone, Qdrant, pgvector, and Weaviate, allowing you to build semantic search and recommendation engines across different platforms.

Use cosine similarity to measure orientation when magnitude is irrelevant, and Euclidean distance for absolute spatial distance. This Skill explains how to apply cosine, Euclidean, and dot product metrics based on your specific similarity search requirements.

Yes, you can use sentence-transformers to generate embeddings for Qdrant. The Skill includes sentence-transformers as a dependency to create vector representations that can be stored and queried efficiently using the provided Qdrant client patterns.