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Rag Implementation

wshobson/agents

Solid foundation for building RAG systems that actually work in production. Covers the essential stack: vector databases like Pinecone and Chroma, embedding models including Anthropic's recommended voyage-3-large, and retrieval strategies from basic semantic search to advanced patterns like HyDE and parent document retrieval. The LangGraph examples show real implementation patterns, not toy demos. Includes hybrid search with BM25 plus embeddings, multi-query retrieval for better recall, and contextual compression to reduce noise. If you're building document Q&A, chatbots with external knowledge, or trying to reduce hallucinations with grounded responses, this covers the techniques that matter.

skills.sh installs: 14.4K
GitHub stars: 42.4K
last updated: Aug 21, 2026

Install

$ npx -y skills add wshobson/agents --skill rag-implementation --agent claude-code

Installs into .claude/skills of the current project.

Files

RAG Implementation

Master Retrieval-Augmented Generation (RAG) to build LLM applications that provide accurate, grounded responses using external knowledge sources.

When to Use This Skill

Building Q&A systems over proprietary documents
Creating chatbots with current, factual information
Implementing semantic search with natural language queries
Reducing hallucinations with grounded responses
Enabling LLMs to access domain-specific knowledge
Building documentation assistants
Creating research tools with source citation

Core Components

1. Vector Databases

Purpose: Store and retrieve document embeddings efficiently

Options:

Pinecone: Managed, scalable, serverless
Weaviate: Open-source, hybrid search, GraphQL
Milvus: Local persistence with Lite, or self-hosted server deployments
Chroma: Lightweight, easy to use, local development
Qdrant: Fast, filtered search, Rust-based
pgvector: PostgreSQL extension, SQL integration

2. Embeddings

Purpose: Convert text to numerical vectors for similarity search

SKILL.md on GitHub

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Details

Repository: github.com/wshobson/agents
Categories: DevOps & CI/CD, AI & Agent Building

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