Can this help improve RAG retrieval accuracy?

Yes, it provides patterns for cross-encoder reranking and hybrid search to significantly improve the relevance of retrieved context.

How does it handle distance metrics?

It provides a clear framework for choosing between Cosine similarity, Euclidean (L2) distance, and Dot Product based on your embedding model.

What vector databases are supported by this skill?

The skill includes production-ready implementation patterns for Pinecone, Qdrant, and pgvector (PostgreSQL).

Does it provide guidance on indexing strategies?

Yes, it covers when to use Flat, HNSW, or IVF+PQ indexes based on your specific requirements for speed, memory, and recall.

Similarity Search Patterns

Name: Similarity Search Patterns
Author: pur3v4d3r

bypur3v4d3r

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Ciencia de Datos y ML

Implements high-performance similarity search and vector retrieval patterns for RAG and semantic search applications.

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This skill provides standardized implementation patterns for vector similarity search across industry-leading databases like Pinecone, Qdrant, and pgvector. It equips developers with optimized templates for upserting embeddings, performing nearest-neighbor queries, and implementing advanced techniques such as hybrid search and cross-encoder reranking. Whether you are scaling a RAG system to millions of vectors or optimizing search latency, this skill offers the domain-specific guidance needed to choose the right distance metrics and indexing strategies for production-grade AI applications.

Características Principales

1 GitHub stars
Guidance on selecting distance metrics like Cosine and Euclidean
Support for hybrid search combining semantic and keyword retrieval
Implementation patterns for cross-encoder reranking
Optimization strategies for HNSW, IVF+PQ, and flat indexing
Multi-database templates for Pinecone, Qdrant, and pgvector

Casos de Uso

Developing high-performance recommendation engines based on vector embeddings
Implementing semantic search for large-scale document repositories
Building production-ready Retrieval-Augmented Generation (RAG) pipelines

Acerca de

Características Principales

1 GitHub stars
Guidance on selecting distance metrics like Cosine and Euclidean
Support for hybrid search combining semantic and keyword retrieval
Implementation patterns for cross-encoder reranking
Optimization strategies for HNSW, IVF+PQ, and flat indexing
Multi-database templates for Pinecone, Qdrant, and pgvector

Casos de Uso

Developing high-performance recommendation engines based on vector embeddings
Implementing semantic search for large-scale document repositories
Building production-ready Retrieval-Augmented Generation (RAG) pipelines