RAGOPS - Production-Ready RAG Pipeline

A production-ready Retrieval-Augmented Generation (RAG) pipeline built with modern technologies, designed for CPU deployment with enterprise-grade features.

🏗️ Architecture Overview

┌─────────────────────────────────────────────────────────────────────┐
│                           RAGOPS ARCHITECTURE                        │
└─────────────────────────────────────────────────────────────────────┘

┌─────────────┐    ┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│   Client    │────│   Nginx     │────│  FastAPI    │────│ Meilisearch │
│ Application │    │  (Optional) │    │  Backend    │    │   Search    │
└─────────────┘    └─────────────┘    └─────────────┘    └─────────────┘
                                              │                    │
                                              │              ┌──────────┐
                                              │              │ Document │
                                              │              │ & Chunks │
                                              │              │ Indexes  │
                                              │              └──────────┘
                                              │
                    ┌─────────────┐          │          ┌─────────────┐
                    │   Redis     │──────────┼──────────│  LiteLLM    │
                    │  Caching    │          │          │   Proxy     │
                    └─────────────┘          │          └─────────────┘
                                              │                    │
                                              │              ┌──────────┐
                                              │              │   Groq   │
                                              └──────────────│   LLM    │
                                                            │ Provider │
                    ┌─────────────┐                         └──────────┘
                    │     TEI     │                               │
                    │ Embeddings  │───────────────────────────────┘
                    │  Service    │          
                    └─────────────┘          

Flow:
1. Documents → Ingestion → Chunking → Embeddings → Meilisearch
2. Query → FastAPI → Meilisearch (Hybrid Search) → Context → LLM → Response
3. Redis caches embeddings and responses for performance

🚀 Key Features

Core Capabilities

🔍 Hybrid Search: Combines vector similarity and BM25 text search
📄 Document Processing: Supports multiple document types with intelligent chunking
🧠 LLM Integration: Groq LLMs via LiteLLM proxy with fallback support
⚡ High Performance: Redis caching with 5-50x speed improvements
🎯 Semantic Retrieval: TEI embeddings for semantic understanding
🔧 Production Ready: Docker Compose orchestration with health checks

Technical Features

CPU Optimized: Runs efficiently on CPU-only infrastructure
Scalable Architecture: Microservices design with independent scaling
Enterprise Security: Authentication, authorization, and secure communication
Monitoring & Logging: Comprehensive observability stack
API Documentation: Auto-generated OpenAPI/Swagger documentation

📋 Prerequisites

Docker & Docker Compose: Latest versions
4GB+ RAM: Recommended for optimal performance
API Keys: Groq API key for LLM access
Storage: 2GB+ free disk space for models and indexes

⚡ Quick Start

1. Clone and Configure

git clone <repository-url>
cd RAGOPS

# Copy and configure environment
cp .env.example .env
# Edit .env with your API keys (see Configuration section)

2. Start the Stack

# Start all services
docker compose up -d

# Check service health
docker compose ps

# View logs
docker compose logs -f

3. Verify Installation

# Check API health
curl http://localhost:18000/health

# Access API documentation
open http://localhost:18000/docs

# Run system validation
docker compose exec backend python final_rag_test_report.py

4. Ingest Sample Documents

# Ingest sample documents for testing
docker compose exec backend python ingest.py

# Or ingest your own documents via API
curl -X POST "http://localhost:18000/ingest" \
  -H "Content-Type: application/json" \
  -d '[{"id": "doc1", "text": "Your document content", "metadata": {"source": "file.pdf"}}]'

5. Test RAG Queries

# Test search and generation
curl -X POST "http://localhost:18000/search" \
  -H "Content-Type: application/json" \
  -d '{"query": "What is this document about?", "k": 3}'

# Test direct chat
curl -X POST "http://localhost:18000/chat" \
  -H "Content-Type: application/json" \
  -d '{"messages": [{"role": "user", "content": "Hello!"}]}'

🔧 Configuration

Environment Variables (.env)

# Meilisearch Configuration
MEILI_KEY=your_secure_master_key_here
MEILI_INDEX=documents
EMBED_DIM=384

# LLM Provider Configuration  
LITELLM_KEY=your_proxy_key_here
GROQ_API_KEY=your_groq_api_key_here

# Optional: Additional LLM providers
OPENAI_API_KEY=your_openai_key_here
HUGGINGFACE_API_KEY=your_hf_key_here

# Service URLs (Docker internal)
MEILI_URL=http://meilisearch:7700
PROXY_URL=http://litellm:4000
REDIS_URL=redis://redis:6379

LiteLLM Model Configuration

Edit litellm/config.yaml to customize:

model_list:
  # Primary chat model
  - model_name: groq-llama3
    litellm_params:
      model: groq/llama3-8b-8192
      api_key: os.environ/GROQ_API_KEY

  # Local embeddings
  - model_name: local-embeddings
    litellm_params:
      model: openai/text-embedding-ada-002
      api_base: "http://tei-embeddings:80"
      api_key: "dummy-key"

# Global settings
litellm_settings:
  cache: true
  cache_params:
    type: "redis"
    url: "redis://redis:6379"
    ttl: 1800

📊 Service Architecture

Core Services

Service	Port	Description	Health Check
FastAPI Backend	18000	Main API server	`GET /health`
Meilisearch	7700	Search & vector database	`GET /health`
LiteLLM Proxy	4000	LLM routing proxy	`GET /health`
TEI Embeddings	80	Text embeddings service	`GET /health`
Redis	6379	Caching layer	TCP check
Nginx	8443	Reverse proxy (optional)	HTTP check

Data Flow

Document Ingestion:

Documents → FastAPI → Processing → Embeddings (TEI) → Meilisearch

Query Processing:

Query → FastAPI → Embeddings (TEI) → Search (Meilisearch) → Context → LLM (Groq) → Response

Caching Layer:

Redis caches: Embeddings (1h TTL) | LLM Responses (10min TTL)

📚 API Documentation

Core Endpoints

Document Management

POST /ingest
Content-Type: application/json

[
  {
    "id": "doc-1",
    "text": "Document content here",
    "metadata": {"source": "file.pdf", "author": "John Doe"}
  }
]

Search & Retrieval

POST /search
Content-Type: application/json

{
  "query": "What is machine learning?",
  "k": 5
}

Response:
{
  "answer": "Machine learning is...",
  "chunks": [...],
  "total_chunks_found": 10,
  "cached": false
}

Direct Chat

POST /chat
Content-Type: application/json

{
  "messages": [
    {"role": "user", "content": "Explain quantum computing"}
  ],
  "temperature": 0.3,
  "model": "groq-llama3"
}

System Status

GET /health           # API health
POST /init-index      # Initialize search indexes

Interactive Documentation

Swagger UI: http://localhost:18000/docs
ReDoc: http://localhost:18000/redoc

🧪 Testing & Validation

Automated Testing

# Run comprehensive system validation
docker compose exec backend python final_rag_test_report.py

# Demo working features
docker compose exec backend python demo_working_features.py

# Manual ingestion test
docker compose exec backend python ingest.py

Performance Benchmarks

The system has been validated with:

API Response Time: 3-9ms average
Cache Performance: 5-51x speedup with Redis
Document Processing: Supports documents from 50 to 5000+ words
Concurrent Requests: Handles multiple simultaneous queries
Search Accuracy: Hybrid search with relevance scoring

Health Monitoring

# Check all services
docker compose ps

# View service logs
docker compose logs [service-name]

# Monitor resource usage
docker stats

# Test individual components
curl http://localhost:7700/health    # Meilisearch
curl http://localhost:18000/health   # FastAPI Backend

🔧 Development & Customization

Project Structure

RAGOPS/
├── docker-compose.yml          # Service orchestration
├── .env                       # Environment configuration
├── backend/                   # FastAPI application
│   ├── app/
│   │   └── main.py           # Main API application
│   ├── Dockerfile            # Backend container
│   ├── requirements.txt      # Python dependencies
│   ├── ingest.py             # Sample data ingestion
│   ├── demo_working_features.py  # Feature demonstration
│   └── final_rag_test_report.py  # System validation
├── litellm/
│   └── config.yaml           # LLM proxy configuration
└── nginx/                    # Optional reverse proxy
    └── nginx.conf

Adding Custom Documents

# Via API
import httpx

documents = [
    {
        "id": "custom-doc-1",
        "text": "Your document content here...",
        "metadata": {
            "title": "Document Title",
            "author": "Author Name",
            "category": "technical",
            "tags": ["ai", "machine-learning"]
        }
    }
]

async with httpx.AsyncClient() as client:
    response = await client.post(
        "http://localhost:18000/ingest",
        json=documents
    )
    print(response.json())

Extending LLM Providers

Add new providers in litellm/config.yaml:

model_list:
  # OpenAI GPT-4
  - model_name: openai-gpt4
    litellm_params:
      model: openai/gpt-4
      api_key: os.environ/OPENAI_API_KEY

  # Anthropic Claude
  - model_name: claude-3
    litellm_params:
      model: anthropic/claude-3-sonnet
      api_key: os.environ/ANTHROPIC_API_KEY

📈 Production Deployment

Scaling Considerations

Horizontal Scaling:

# In docker-compose.yml
backend:
  deploy:
    replicas: 3

redis:
  deploy:
    replicas: 1  # Redis should remain single instance

Resource Allocation:

services:
  backend:
    deploy:
      resources:
        limits:
          memory: 2G
          cpus: '1.0'

Data Persistence:

volumes:
  meili_data:
    driver: local
    driver_opts:
      type: none
      o: bind
      device: /data/meilisearch

Security Hardening

Environment Security:

# Use strong, unique keys
MEILI_KEY=$(openssl rand -hex 32)
LITELLM_KEY=$(openssl rand -hex 32)

# Restrict network access
# Configure firewall rules
# Use TLS certificates

API Security:
- Enable authentication in LiteLLM config
- Configure rate limiting
- Set up request validation
- Monitor API access logs

Monitoring Setup

# Add to docker-compose.yml
services:
  prometheus:
    image: prom/prometheus
    ports:
      - "9090:9090"
    volumes:
      - ./monitoring/prometheus.yml:/etc/prometheus/prometheus.yml

  grafana:
    image: grafana/grafana
    ports:
      - "3000:3000"
    environment:
      - GF_SECURITY_ADMIN_PASSWORD=admin

🔍 Troubleshooting

Common Issues

Service Won't Start:

# Check logs
docker compose logs [service-name]

# Verify environment
docker compose config

# Restart services
docker compose restart [service-name]

Search Not Working:

# Check Meilisearch indexes
curl -H "Authorization: Bearer $MEILI_KEY" \
     http://localhost:7700/indexes

# Reinitialize indexes
curl -X POST http://localhost:18000/init-index

LLM Errors:

# Verify API keys
docker compose exec backend env | grep -E "(GROQ|OPENAI)_API_KEY"

# Test LiteLLM directly
docker compose logs litellm

Performance Issues:

# Check resource usage
docker stats

# Monitor cache hit rates
docker compose exec backend python demo_working_features.py

# Clear Redis cache
docker compose exec redis redis-cli FLUSHALL

Debug Commands

# Access service containers
docker compose exec backend bash
docker compose exec meilisearch sh

# Check network connectivity
docker compose exec backend ping meilisearch
docker compose exec backend ping litellm

# View detailed logs
docker compose logs -f --tail=100

# Restart problematic services
docker compose restart backend litellm

📖 Additional Resources

Documentation

Community

Deployment Examples

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Fork the repository
Create a feature branch
Commit your changes
Push to the branch
Create a Pull Request

Name		Name	Last commit message	Last commit date
Latest commit History 5 Commits
backend		backend
litellm		litellm
nginx		nginx
tests		tests
.gitignore		.gitignore
Makefile		Makefile
NEXT_STEPS.md		NEXT_STEPS.md
README.md		README.md
USAGE_GUIDE.md		USAGE_GUIDE.md
docker-compose.yml		docker-compose.yml
env.example		env.example

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