Agent skill · research science · wshobson
rag-implementation
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
Why this skill is useful
Includes executable scripts for building RAG systems with vector databases and specific retrieval strategies that extend AI capabilities.
What it needs
Requires langchain, langgraph, pinecone installed locally. Requires pinecone account access. About 2k tokens when loaded. Last updated 2026-08-05. 38,570 stars on the source repository.
What this skill does
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: High performance, on-premise 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 Models (2026): Model Dimensions Best For ------- ------------ ---------- voyage-3-large 1024 Claude apps (Anthropic recommended) voyage-code-3 1024 Code search text-embedding-3-large 3072 OpenAI apps, high accuracy text-embedding-3-small 1536 OpenAI apps, cost-effective bge-large-en-v1.5 1024 Open source, local deployment multilingual-e5-large 1024 Multi-language support 3. Retrieval Strategies Approaches: Dense Retrieval: Semantic similarity via embeddings Sparse Retrieval: Keyword matching (BM25, TF-IDF) Hybrid Search: Combine dense + sparse with weighted fusion Multi-Query: Generate multiple query variations HyDE: Generate hypothetical documents for better retrieval 4. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills wshobson/rag-implementation