Rag Implementation

Name: Rag Implementation
Author: LoopyLuci

ASecurity

Build retrieval augmented generation with embeddings and vector DB

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Added September 10, 2026

ai-agentspython

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A100/100

Scanned September 10, 2026

npx -y skills add LoopyLuci/Skills --skill rag-implementation --agent claude-code

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SKILL.md

---
name: rag-implementation
description: "Build retrieval augmented generation with embeddings and vector DB"
---

# RAG Implementation

## Pipeline
1. Chunk documents
2. Generate embeddings
3. Store in vector DB
4. Query: embed question + search + context + LLM

## Minimal RAG
```python
from sentence_transformers import SentenceTransformer
import chromadb

model = SentenceTransformer("all-MiniLM-L6-v2")
client = chromadb.Client()
collection = client.create_collection("docs")

# Index
collection.add(documents=["doc1 text", "doc2 text"], ids=["1", "2"])

# Query
results = collection.query(query_texts=["user question"], n_results=3)
context = "
".join(results["documents"][0])
prompt = f"Context: {context}
Question: ..."
```