# RAG Vector DB

> Retrieval-Augmented Generation implementation using vector databases, embedding models, and query routines.

- Skill: `lord1egypt/rag-vector-db` (Agent Skill)
- Install (CLI): `npx skillmds@latest add lord1egypt/rag-vector-db`
- Raw SKILL.md: https://api.skillmd.com/api/skills/lord1egypt/rag-vector-db/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT license
- Author: Lord1Egypt (https://skillmd.com/u/lord1egypt)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/lord1egypt/rag-vector-db

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# Rag Vector Db

## Overview
RAG enhances LLM outputs by retrieving relevant documents from an external vector index based on query embeddings.

## When to Use This Skill
Use to build custom Q&A pipelines over proprietary documentation files or PDF sets.

## Quick Start (with runnable code examples)

```python
# Simple cosine similarity matching using numpy (conceptual vector lookup)
import numpy as np

def cosine_similarity(v1, v2):
    return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))

v_query = np.array([0.1, 0.2, 0.9])
v_doc = np.array([0.11, 0.19, 0.88])
print("Match Score:", cosine_similarity(v_query, v_doc))
```

## Advanced Usage
Integrate ChromaDB/Pinecone clients, slice texts using recursive chunking algorithms, and build reranking layers.

## Key References
- [LlamaIndex Documentation](https://www.llamaindex.ai/)

## Dependencies
- numpy>=1.20.0