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 for building RAG systems with specific vector databases and embedding models that enhance LLM capabilities.
What it needs
Requires langchain installed locally. 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. Use this skill when 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 Do not use this skill when You only need purely generative writing without retrieval The dataset is too small to justify embeddings You cannot store or process the source data safely Instructions 1. Define the corpus, update cadence, and evaluation targets. 2. Choose embedding models and vector store based on scale. 3. Build ingestion, chunking, and retrieval with reranking. 4. Evaluate with grounded QA metrics and monitor drift. Safety Redact sensitive data and enforce access controls. Avoid exposing source documents in responses when restricted. 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