Multi-Purpose NLP Services 🤖

NLP.Service.Dashboard.webm

Project Overview

A Natural Language Processing (NLP) service providing comprehensive text analysis capabilities through a REST-like API interface. Built with FastAPI and powered by advanced language models including Llama3.2, this service delivers robust and efficient NLP solutions.

Core Services

1. 🎭 Sentiment Analysis

• Model: Llama3.2:3b via Ollama
• Key Features:
  • Sophisticated sentiment detection (POSITIVE/NEGATIVE/NEUTRAL)
  • High-precision confidence scoring
  • Contextual sentiment understanding
  • Detailed sentiment breakdown and metadata

2. 🎯 Named Entity Recognition (NER)

• Model: Custom fine-tuned TinyLlama
• Capabilities:
  • Core entity detection (PERSON, ORG, LOC)
  • Extended entity types (TIME, NUMBER, EMAIL)
  • Precise position tracking
  • Multi-language support

3. 📚 Text Summarization

• Model: Llama3.2:3b via Ollama
• Features:
  • Dual mode: Abstractive & Extractive
  • Dynamic length control
  • Key points extraction
  • Compression ratio analysis

4. 📑 Text Classification

• Model: Llama3.2:3b via Ollama
• Highlights:
  • Multi-label classification support
  • Customizable category system
  • Confidence scoring with explanations
  • Balanced category distribution

Technical Implementation

Performance Optimization

• Redis-based Caching System
  • Service-specific cache timeouts
  • Intelligent cache key generation
  • Automatic cache invalidation
  • Performance monitoring

Robust Architecture

• Error Management:
  • Custom exception hierarchy
  • Comprehensive error tracking
  • Graceful degradation
  • Detailed error reporting

Quality Assurance

• Testing Suite:
  • Comprehensive unit testing
  • Integration test coverage
  • Load testing with Locust
  • Continuous monitoring

Developer Experience

• Documentation:
  • Interactive Swagger UI
  • Comprehensive ReDoc
  • OpenAPI specifications
  • Usage examples

Deployement Setup

• Containerization and CI/CD
  • Multi container Microservice approach
  • microservices: frontend, backend, redis
  • CI/CD using github actions

Future Work

1. Model Enhancements

• Model quantization for faster inference
• Support for newer model architectures
• Custom model fine-tuning options
• Multi-model ensemble support

2. Feature Expansion

• Text similarity analysis
• Language detection
• Toxicity detection
• Custom plugin system

3. Performance Optimization

• Batch processing implementation
• Async processing pipeline
• GPU acceleration support
• Distributed processing capability

4. Infrastructure

• Kubernetes deployment configurations
• Monitoring dashboard
• Auto-scaling implementation

Technical Stack

• Backend: FastAPI, Python 3.10+
• Models: Llama3.2, TinyLlama, Phi3, Gemma2, Qwen2.5
• Caching: Redis
• UI: Streamlit
• Testing: Pytest, Locust
• Documentation: OpenAPI, ReDoc

Project Status

🟢 Active Development

• Core services: Completed
• Caching system: Implemented
• Error handling: Implemented
• Documentation: Completed
• Testing: Implemented
• UI/UX: Completed

License

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