# RAG System Builder Step 1 Vector Embeddings Table

> Sub-skill of rag-system-builder: Step 1: Vector Embeddings Table (+4).

- Skill: `vamseeachanta/rag-system-builder-step-1-vector-embeddings-table` (Agent Skill)
- Install (CLI): `npx skillmds@latest add vamseeachanta/rag-system-builder-step-1-vector-embeddings-table`
- Raw SKILL.md: https://api.skillmd.com/api/skills/vamseeachanta/rag-system-builder-step-1-vector-embeddings-table/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: vamseeachanta (https://skillmd.com/u/vamseeachanta)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/vamseeachanta/rag-system-builder-step-1-vector-embeddings-table

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# Step 1: Vector Embeddings Table (+4)

## Step 1: Vector Embeddings Table


```python
import sqlite3
import numpy as np

def setup_embeddings_table(db_path):
    conn = sqlite3.connect(db_path, timeout=30)
    cursor = conn.cursor()

    cursor.execute('''
        CREATE TABLE IF NOT EXISTS embeddings (

*See sub-skills for full details.*

## Step 2: Generate Embeddings


```python
from sentence_transformers import SentenceTransformer
import numpy as np

class EmbeddingGenerator:
    def __init__(self, model_name='all-MiniLM-L6-v2'):
        self.model = SentenceTransformer(model_name)
        self.dimension = 384  # all-MiniLM-L6-v2

    def embed_text(self, text):

*See sub-skills for full details.*

## Step 3: Semantic Search


```python
def semantic_search(db_path, query, model, top_k=5):
    """Find most similar chunks to query."""
    conn = sqlite3.connect(db_path, timeout=30)
    cursor = conn.cursor()

    # Embed query
    query_embedding = model.embed_text(query)

    # Get all embeddings

*See sub-skills for full details.*

## Step 4: RAG Query Engine


```python
import anthropic
import openai

class RAGQueryEngine:
    def __init__(self, db_path, embedding_model):
        self.db_path = db_path
        self.model = embedding_model

    def query(self, question, top_k=5, provider='anthropic'):

*See sub-skills for full details.*

## Step 5: CLI Interface


```python
#!/usr/bin/env python3
"""RAG Query CLI - Ask questions about your documents."""

import argparse
import os

def main():
    parser = argparse.ArgumentParser(description='RAG Q&A System')
    parser.add_argument('question', nargs='?', help='Question to ask')

*See sub-skills for full details.*