Your RAG prototype works on your laptop. But deploying to production introduces challenges: latency, scaling, document updates, and monitoring. This deployment guide covers the full journey.

💡 What You Will Learn

Your RAG prototype works on your laptop. But deploying to production introduces challenges: latency, scaling, document updates, and monitoring. This deployment guide covers the full journey.

📜 Table of Contents

• Architecture Overview
• Step 1: Choose Your Vector Database
• Step 3: Production Deployment with Qdrant
• Step 4: Docker Compose for Production
• Performance Benchmarks

RAG Pipeline Deployment Guide: From Local Prototype to Production with Vector Databases

RAG (Retrieval-Augmented Generation) is the most popular pattern for grounding LLMs in your own data. Here's the complete deployment guide.

Architecture Overview

User Query → Embedding Model → Vector Search → Context Retrieval → Prompt Assembly → LLM Generation → Final Response

Step 1: Choose Your Vector Database

import chromadb
from chromadb.utils import embedding_functions

client = chromadb.Client()
collection = client.create_collection(
    name="my_docs",
    embedding_function=embedding_functions.SentenceTransformerEmbeddingFunction(
        model_name="all-MiniLM-L6-v2"
    )
)
collection.add(documents=["Your document content here..."], ids=["doc1"])
results = collection.query(query_texts=["your question"], n_results=3)

Step 3: Production Deployment with Qdrant

from qdrant_client import QdrantClient
from qdrant_client.models import VectorParams, Distance

client = QdrantClient(host="localhost", port=6333)
client.create_collection(
    collection_name="production_docs",
    vectors_config=VectorParams(size=384, distance=Distance.COSINE)
)

Step 4: Docker Compose for Production

services:
  qdrant:
    image: qdrant/qdrant:latest
    ports: ["6333:6333"]
  llm-service:
    image: vllm/vllm-openai:latest
    command: --model Qwen/Qwen2.5-7B-Instruct
    ports: ["8000:8000"]
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

Performance Benchmarks

❓ FAQ

How often to update vector DB?

Real-time for dynamic data, batch nightly for static docs.

Best embedding model?

BGE-small-en-v1.5 (speed) or BGE-base-en-v1.5 (quality).

How to handle large documents?

Chunk into 512-token segments with 50-token overlap.