A production-ready, scalable multi-agent RAG system demonstrating advanced ML/GenAI capabilities for enterprise applications.

This project showcases a comprehensive AI/ML platform that includes:

• Multi-Agent RAG System: Advanced retrieval-augmented generation with orchestrated agents
• LLM Fine-tuning Pipeline: LoRA/PEFT-based fine-tuning for enterprise models
• Production APIs: Robust FastAPI services with monitoring and governance
• MLOps Integration: Full CI/CD pipeline with drift detection and auto-retraining
• Cloud-Native Architecture: Deployment configs for Azure OpenAI and AWS Bedrock
• Vector Database Integration: Support for Pinecone, Milvus, and Elasticsearch
• Responsible AI: Built-in governance, explainability, and ethical AI practices

┌─────────────────────────────────────────────────────────────┐
│                      API Gateway (FastAPI)                  │
├─────────────────────────────────────────────────────────────┤
│  Multi-Agent Orchestration Layer (LangGraph/AutoGen)       │
├──────────────┬──────────────┬──────────────┬───────────────┤
│ Research     │ Code         │ Analytics    │ Orchestrator  │
│ Agent        │ Agent        │ Agent        │ Agent         │
└──────┬───────┴──────┬───────┴──────┬───────┴───────┬───────┘
       │              │              │               │
       └──────────────┴──────────────┴───────────────┘
                      │
       ┌──────────────┴──────────────┐
       │   RAG Pipeline Engine       │
       ├─────────────────────────────┤
       │ - Document Processing       │
       │ - Embedding Generation      │
       │ - Vector Search             │
       │ - Context Retrieval         │
       └──────────────┬──────────────┘
                      │
       ┌──────────────┴──────────────┐
       │   LLM Layer                 │
       ├─────────────────────────────┤
       │ - GPT-4/GPT-3.5            │
       │ - Llama 3/3.1              │
       │ - Mistral                  │
       │ - Fine-tuned Models        │
       └─────────────────────────────┘

.
├── src/
│   ├── agents/              # Multi-agent framework
│   ├── rag/                 # RAG pipeline components
│   ├── models/              # Model definitions and fine-tuning
│   ├── api/                 # FastAPI services
│   ├── mlops/               # MLOps utilities
│   ├── cloud/               # Cloud integrations
│   └── utils/               # Shared utilities
├── config/                  # Configuration files
├── deployment/              # Kubernetes, Docker configs
├── notebooks/               # Jupyter notebooks for experimentation
├── tests/                   # Unit and integration tests
├── data/                    # Sample data and artifacts
├── models/                  # Trained model artifacts
└── docs/                    # Additional documentation

• Agentic Workflows: Tool-augmented reasoning and orchestration
• Memory Management: Persistent and contextual memory
• Agent Collaboration: Dynamic task routing and coordination
• Frameworks: LangGraph, AutoGen, CrewAI integration

• Document Processing: Multiple format support (PDF, DOCX, HTML, MD)
• Chunking Strategies: Semantic, recursive, and custom chunking
• Embeddings: OpenAI, Hugging Face, Azure OpenAI
• Vector Databases: Pinecone, Milvus, Elasticsearch, Chroma
• Hybrid Search: Dense + sparse retrieval

• LoRA/QLoRA: Parameter-efficient fine-tuning
• PEFT Methods: Prefix tuning, adapter layers
• Quantization: 4-bit, 8-bit quantization support
• Models: Llama 3, Mistral, GPT variants

• FastAPI: High-performance async APIs
• Authentication: JWT, API keys, OAuth2
• Rate Limiting: Token bucket algorithm
• Monitoring: Prometheus, Grafana integration
• Governance: Request logging, audit trails

• CI/CD: GitHub Actions, Azure DevOps
• Model Versioning: MLflow integration
• Drift Detection: Statistical and performance-based
• Auto-Retraining: Scheduled and trigger-based
• A/B Testing: Model comparison framework

• Azure OpenAI: Seamless integration
• AWS Bedrock: Multi-model support
• Kubernetes: Production-grade orchestration
• Docker: Multi-stage optimized builds

• Python: 3.11+
• PyTorch: 2.x
• Transformers: Hugging Face
• LangChain: 0.1.x
• LangGraph: Latest
• AutoGen: Latest
• CrewAI: Latest

• Pinecone
• Milvus
• Elasticsearch
• ChromaDB

• MLflow
• Weights & Biases
• Docker & Kubernetes
• Prometheus & Grafana
• Redis (caching)
• PostgreSQL (metadata)

• Azure OpenAI Service
• AWS Bedrock
• Azure ML
• AWS SageMaker

• Python 3.11+
• Docker Desktop
• Kubernetes (minikube or cloud cluster)
• Azure/AWS CLI (for cloud deployment)

# Clone the repository
git clone <repo-url>
cd ericson

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Install development dependencies
pip install -r requirements-dev.txt

# Set up environment variables
cp .env.example .env
# Edit .env with your API keys and configurations

# Initialize vector database
python scripts/setup_vectordb.py

# Run database migrations
alembic upgrade head

Create a .env file with the following:

# LLM Providers
OPENAI_API_KEY=your_key_here
AZURE_OPENAI_KEY=your_key_here
AZURE_OPENAI_ENDPOINT=your_endpoint_here
AWS_ACCESS_KEY_ID=your_key_here
AWS_SECRET_ACCESS_KEY=your_secret_here

# Vector Databases
PINECONE_API_KEY=your_key_here
PINECONE_ENVIRONMENT=your_env_here
MILVUS_HOST=localhost
MILVUS_PORT=19530

# MLOps
MLFLOW_TRACKING_URI=http://localhost:5000
WANDB_API_KEY=your_key_here

# Application
API_HOST=0.0.0.0
API_PORT=8000
ENVIRONMENT=development

# Development mode with hot reload
uvicorn src.api.main:app --reload --host 0.0.0.0 --port 8000

# Production mode
gunicorn src.api.main:app -w 4 -k uvicorn.workers.UvicornWorker

from src.agents.orchestrator import AgentOrchestrator
from src.rag.pipeline import RAGPipeline

# Initialize RAG pipeline
rag = RAGPipeline(
    vector_db="pinecone",
    embedding_model="text-embedding-3-large"
)

# Create agent orchestrator
orchestrator = AgentOrchestrator(rag_pipeline=rag)

# Execute multi-agent workflow
result = orchestrator.execute(
    query="Analyze quarterly revenue trends and generate insights",
    agents=["research", "analytics", "report_writer"]
)

print(result)

# Fine-tune Llama 3 with LoRA
python src/models/finetune.py \
    --model_name meta-llama/Llama-3-8b \
    --dataset data/training_data.json \
    --method lora \
    --rank 8 \
    --alpha 16 \
    --epochs 3

from src.rag.query_engine import QueryEngine

engine = QueryEngine()

response = engine.query(
    question="What are the key features of our product?",
    top_k=5,
    rerank=True
)

print(f"Answer: {response.answer}")
print(f"Sources: {response.sources}")

# Run all tests
pytest tests/

# Run with coverage
pytest tests/ --cov=src --cov-report=html

# Run specific test suite
pytest tests/test_agents.py -v

# Build image
docker build -t aiml-platform:latest .

# Run container
docker run -p 8000:8000 --env-file .env aiml-platform:latest

# Apply configurations
kubectl apply -f deployment/k8s/namespace.yaml
kubectl apply -f deployment/k8s/configmap.yaml
kubectl apply -f deployment/k8s/secrets.yaml
kubectl apply -f deployment/k8s/deployment.yaml
kubectl apply -f deployment/k8s/service.yaml

# Check status
kubectl get pods -n aiml-platform

Azure:

# Deploy to Azure Container Apps
az containerapp up \
    --name aiml-platform \
    --resource-group aiml-rg \
    --location eastus \
    --environment aiml-env \
    --image <your-acr>.azurecr.io/aiml-platform:latest

AWS:

# Deploy to ECS
aws ecs create-service \
    --cluster aiml-cluster \
    --service-name aiml-platform \
    --task-definition aiml-platform:1 \
    --desired-count 3

Access monitoring dashboards:

• API Metrics: http://localhost:3000 (Grafana)
• MLflow: http://localhost:5000
• Prometheus: http://localhost:9090

This project implements:

• Bias Detection: Automated fairness testing
• Explainability: SHAP, LIME integration
• Privacy: PII detection and redaction
• Governance: Audit logging and compliance tracking
• Content Safety: Azure Content Safety integration

• API Documentation
• Agent Framework Guide
• RAG Pipeline Details
• Fine-tuning Guide
• MLOps Best Practices
• Cloud Deployment Guide

See CONTRIBUTING.md for guidelines.

MIT License - See LICENSE file

For questions or support, reach out to the development team.

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