Agent skill · research science · aiskillstore
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
Adds executable scripts and advanced patterns for implementing RAG systems that integrate LLMs with external knowledge sources.
What it needs
Requires langchain installed locally. Requires openai account access. About 5k tokens when loaded. Last updated 2026-08-07. 405 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, fast queries Weaviate: Open-source, hybrid search Milvus: High performance, on-premise Chroma: Lightweight, easy to use Qdrant: Fast, filtered search FAISS: Meta's library, local deployment 2. Embeddings Purpose: Convert text to numerical vectors for similarity search Models: text-embedding-ada-002 (OpenAI): General purpose, 1536 dims all-MiniLM-L6-v2 (Sentence Transformers): Fast, lightweight e5-large-v2: High quality, multilingual Instructor: Task-specific instructions bge-large-en-v1.5: SOTA performance 3. Retrieval Strategies Approaches: Dense Retrieval: Semantic similarity via embeddings Sparse Retrieval: Keyword matching (BM25, TF-IDF) Hybrid Search: Combine dense + sparse Multi-Query: Generate multiple query variations HyDE: Generate hypothetical documents 4. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/rag-implementation--a84271