What is the best chunking strategy for RAG systems?

A recommended starting point is 512 tokens with a 50-token (10%) overlap, which balances capturing enough context for the LLM while maintaining retrieval precision.

Which vector database is recommended for most AI applications?

Qdrant is the recommended primary choice due to its superior metadata filtering, built-in hybrid search capabilities, and flexible hosting options ranging from Docker to managed cloud.

Does this skill support hybrid search implementation?

Yes, it provides implementation patterns for combining semantic vector search with BM25 keyword matching using Reciprocal Rank Fusion for the most accurate results.

How do I evaluate the quality of my vector retrieval?

The skill includes automated evaluation patterns using the RAGAS framework to measure metrics such as faithfulness, answer relevancy, context recall, and precision.

Vector Database Implementation

Name: Vector Database Implementation
Author: ancoleman

byancoleman

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158

数据科学与机器学习

Implements high-performance vector databases and retrieval-augmented generation (RAG) patterns for AI-powered applications.

关于

This skill provides a comprehensive framework for architecting and deploying vector search solutions within AI applications. It offers a standardized decision framework for selecting the ideal database—from Qdrant and Pinecone to pgvector and Milvus—while providing technical patterns for embedding model selection, hybrid search implementation, and optimal document chunking. Designed for developers building RAG systems, semantic search engines, and recommendation models, it ensures retrieval accuracy through advanced metadata filtering and automated evaluation using RAGAS metrics.

主要功能

Automated RAG evaluation workflows using faithfulness and relevancy metrics
Production-grade metadata schemas for advanced pre-filtering and multi-tenancy
Hybrid search patterns combining semantic vector similarity with BM25 keywords
Multi-vendor database selection framework (Qdrant, Pinecone, pgvector, Milvus)
158 GitHub stars
Optimized chunking and embedding strategies for maximum RAG precision

使用场景

Developing high-accuracy code search and recommendation systems
Implementing semantic search capabilities for private documentation and knowledge bases
Building Retrieval-Augmented Generation (RAG) pipelines for enterprise AI chatbots

关于

主要功能

Automated RAG evaluation workflows using faithfulness and relevancy metrics
Production-grade metadata schemas for advanced pre-filtering and multi-tenancy
Hybrid search patterns combining semantic vector similarity with BM25 keywords
Multi-vendor database selection framework (Qdrant, Pinecone, pgvector, Milvus)
158 GitHub stars
Optimized chunking and embedding strategies for maximum RAG precision

使用场景

Developing high-accuracy code search and recommendation systems
Implementing semantic search capabilities for private documentation and knowledge bases
Building Retrieval-Augmented Generation (RAG) pipelines for enterprise AI chatbots