Private RAG Architecture with MySQL and Embeddings

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# Private RAG Architecture with MySQL and Embeddings

## Cabecalho
- Tipo: Conteudo
- Categoria: RAG e LLMs
- Modulos: 0
- Agentes: 0

## Escopo
Technical prompt to create RAG pipelines using MySQL as the vector store. Ideal for companies that need to keep data private without using external services.

## Prompt Principal
Create a private and secure RAG (Retrieval-Augmented Generation) architecture.

## Context
- Company: [NOME_EMPRESA]
- Knowledge base: [TIPO: PDF / banco de dados / wiki]
- Volume: [MB/GB]
- Privacy: maximum (no data leaving for external APIs)
- LLM: [Ollama Llama3 / Claude / GPT-4]

## Architecture

### 1. Ingestion
- Parser for PDF, DOCX, TXT, HTML
- Chunking: 512 tokens, overlap: 50
- Cleaning and normalization
- Metadata per chunk

### 2. Embeddings
- Model: nomic-embed-text or text-embedding-3-small
- Dimension: 768 or 1536
- Batch processing

### 3. MySQL Storage
CREATE TABLE documents (
  id BIGINT PRIMARY KEY AUTO_INCREMENT,
  content TEXT NOT NULL,
  embedding JSON NOT NULL,
  source VARCHAR(255),
  chunk_index INT,
  metadata JSON,
  created_at DATETIME DEFAULT CURRENT_TIMESTAMP
);

### 4. Semantic search
- Cosine similarity in SQL
- Threshold: 0.75
- Top-K: 5
- Reranking by relevance

### 5. Generation
- Context building
- Prompt with citations
- Response with references

### 6. REST API
- POST /query
- POST /ingest
- GET /documents
- DELETE /document/:id

## Stack
- Python 3.11 + FastAPI
- SQLAlchemy + MySQL 8+
- LangChain or LlamaIndex

Generate the complete code for the RAG system.