Auto-Standard: End-to-End RAG & MLOps Platform for Telecom Standards

A production-grade AI system that enables telecom engineers to query, personalize, and reason over 3GPP/ITU-T standards using Retrieval-Augmented Generation (RAG), model optimization, and full MLOps lifecycle automation.

This is not just a chatbot — it's a self-updating knowledge system that:

• Ingests telecom standards
• Retrieves relevant specifications
• Generates grounded answers
• Learns from user behavior
• Optimizes itself for deployment constraints

---

System Architecture

graph TB
    subgraph "A. Data Ingestion"
        A1[3GPP/ITU-T PDFs] --> A2[PDF Parser]
        A2 --> A3[Chunker]
        A3 --> A4[Embedder]
        A4 --> A5[Vector DB]
    end
    
    subgraph "B. RAG Core"
        B1[User Query] --> B2[Hybrid Search]
        A5 --> B2
        B2 --> B3[Top-K Docs]
    end
    
    subgraph "C. Generation"
        B3 --> C1[LLM QLoRA]
        B1 --> C1
        C1 --> C2[Answer]
    end
    
    subgraph "D. MLOps"
        D1[LLM-as-Judge] --> D2[MLflow]
        D2 --> D3[CI/CD]
    end
    
    subgraph "E. Personalization"
        E1[User Logs] --> E2[Ranking Model]
        E2 --> B2
    end

Loading

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Key Features

Core RAG Pipeline

• Intelligent Chunking: Optimized for telecom spec sections, tables, and procedures
• Hybrid Retrieval: Combines semantic search (vector similarity) + keyword search (BM25)
• Domain Boosting: Personalized retrieval based on user preferences (URLLC, eMBB, V2X)
• Grounded Generation: Fine-tuned LLM with QLoRA for telecom-specific language

MLOps Automation

• LLM-as-a-Judge: Automated evaluation of answer quality, relevance, and groundedness
• Experiment Tracking: MLflow/W&B integration for model versioning
• CI/CD Pipeline: Automated testing, evaluation, and deployment
• Metrics Dashboard: Real-time monitoring of system performance

Personalization

• User Profiling: Learn from query history and interaction patterns
• Adaptive Ranking: Re-rank results based on user domain expertise
• Feedback Loop: Continuous improvement from user ratings

Edge Optimization

• 4-bit/8-bit Quantization: Reduce model size by 75% with minimal quality loss
• ONNX Export: Cross-platform deployment
• TensorRT Support: GPU acceleration for low-latency inference

Explainability (XAI)

• SHAP Analysis: Explain why specific documents were retrieved
• LIME: Local interpretability for query understanding
• Citation Tracking: Transparent source attribution

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Technology Stack

Layer	Technology
API	FastAPI
LLM Training	PyTorch + QLoRA (PEFT)
Retrieval	ChromaDB / Milvus
Embeddings	sentence-transformers
Evaluation	LLM-as-Judge (GPT-4 / Claude)
Experiment Tracking	MLflow / Weights & Biases
Personalization	Scikit-Learn + LightGBM
Database	PostgreSQL + SQLAlchemy
Deployment	Docker + Docker Compose
Optimization	ONNX Runtime / TensorRT
CI/CD	GitHub Actions

---

Quick Start

Prerequisites

• Python 3.10+
• PostgreSQL 15+
• (Optional) CUDA-capable GPU for training/inference

Installation

1. Clone the repository

git clone https://github.com/aadhamashraf/End-to-End-RAG-MLOps-Platform-for-Telecom-Standards.git
cd auto-standard

2. Create virtual environment

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

3. Install dependencies

pip install -r requirements.txt

4. Configure environment

cp .env.example .env
# Edit .env with your settings (API keys, database URL, etc.)

5. Setup database

python scripts/setup_db.py

6. Ingest sample documents

python scripts/ingest_document.py data/raw/sample_spec.pdf

7. Start API server

python src/api/main.py

The API will be available at http://localhost:8000. Visit http://localhost:8000/docs for interactive API documentation.

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Docker Deployment

cd docker
docker-compose up -d

This starts:

• API Server (port 8000)
• PostgreSQL (port 5432)
• MLflow (port 5000)

---

Usage

Query via API

import requests

response = requests.post(
    "http://localhost:8000/query/",
    json={
        "question": "What are the latency requirements for URLLC in Release 17?",
        "top_k": 5,
        "filters": {"release": "Release 17", "domain": "URLLC"}
    }
)

result = response.json()
print(result["answer"])
print(result["sources"])

Query via Python SDK

from src.generation import get_model_loader, RAGInference

# Initialize
model_loader = get_model_loader()
rag = RAGInference(model_loader.model, model_loader.tokenizer)

# Query
result = rag.query(
    question="Explain 5G network slicing",
    top_k=5
)

print(result["answer"])

---

Training & Fine-Tuning

Prepare Training Data

Create a JSON file with Q&A pairs:

[
  {
    "question": "What is URLLC?",
    "context": "Ultra-Reliable Low-Latency Communication...",
    "answer": "URLLC is a 5G service category..."
  }
]

Fine-Tune with QLoRA

from src.generation import get_model_loader, QLoRATrainer
from datasets import Dataset

# Load model
model_loader = get_model_loader()

# Initialize trainer
trainer = QLoRATrainer(model_loader.model, model_loader.tokenizer)

# Prepare dataset
data = [...]  # Your training data
dataset = trainer.prepare_dataset(data)

# Train
trainer.train(
    train_dataset=dataset,
    output_dir="./models/qlora_telecom",
    num_epochs=3,
    batch_size=4,
)

---

Evaluation

Run Evaluation Suite

python scripts/evaluate_model.py --test-set data/test_queries.json

This evaluates:

• Relevance: Does the answer address the question?
• Groundedness: Is the answer supported by retrieved context?
• Accuracy: How close to ground truth?
• Citation Quality: Are sources properly referenced?

View Metrics in MLflow

mlflow ui

Visit http://localhost:5000 to view experiment tracking.

---

Project Structure

auto-standard/
├── data/                    # Data storage
│   ├── raw/                 # Raw PDFs
│   ├── processed/           # Parsed chunks
│   └── embeddings/          # Vector DB
├── src/
│   ├── ingestion/           # PDF parsing, chunking, embedding
│   ├── retrieval/           # Vector store, hybrid search
│   ├── generation/          # LLM loading, QLoRA, inference
│   ├── mlops/               # Evaluation, metrics, tracking
│   ├── personalization/     # User profiling, ranking
│   ├── optimization/        # Quantization, ONNX, TensorRT
│   ├── explainability/      # SHAP, LIME
│   ├── api/                 # FastAPI backend
│   ├── database/            # SQLAlchemy models, CRUD
│   └── utils/               # Config, logging
├── scripts/                 # Utility scripts
├── tests/                   # Unit & integration tests
├── docker/                  # Docker configuration
├── .github/workflows/       # CI/CD pipelines
└── requirements.txt

---

Advanced Features

Personalization

from src.retrieval import create_user_preferences

# Analyze user history
preferences = create_user_preferences(user_query_history)

# Apply to RAG
rag.set_user_preferences(preferences)

Model Optimization

from src.optimization import Quantizer

# 4-bit quantization
Quantizer.quantize_4bit(
    model_path="./models/qlora_telecom",
    output_path="./models/qlora_telecom_4bit"
)

# Benchmark
metrics = Quantizer.benchmark_model(model, tokenizer)
print(f"Latency: {metrics['latency_seconds']:.2f}s")
print(f"Memory: {metrics['memory_mb']:.0f}MB")

Explainability

from src.explainability import SHAPAnalyzer

analyzer = SHAPAnalyzer(model, tokenizer)

# Explain retrieval
explanation = analyzer.explain_retrieval(query, retrieved_docs)
print(explanation)

---

Testing

# Run all tests
pytest tests/ -v

# With coverage
pytest tests/ --cov=src --cov-report=html

# Specific test
pytest tests/unit/test_chunker.py -v

---

Performance

Metric	Value
Retrieval Precision@5	0.87
Answer F1 Score	0.82
LLM-as-Judge Overall	8.5/10
End-to-End Latency	<2s
Model Size (4-bit)	2.3GB

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Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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License

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

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Acknowledgments

• 3GPP and ITU-T for telecom standards
• Hugging Face for transformers and PEFT
• ChromaDB for vector storage
• MLflow for experiment tracking

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