Lord1Egypt 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)
# 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
Dependencies
- numpy>=1.20.0