Chatbot Projects Suite 🚀

A repository of conversational AI solutions tailored to diverse domains
Modular, production-ready platforms focusing on scalability and cost optimization.

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🌐 Overview

This repository consolidates three advanced chatbot projects that leverage modern NLP techniques, document management, and contextual analysis. Each solution is designed for specific domains with optimized architectures:

Project	Domain	Primary Stack	Status
ClimateGuardian	Environmental	Python/Docker/PostGIS	Production
Coach CV	Human Resources	React/Flask/spaCy	Beta
DocuBot AWS	Technical Support	LangChain/FAISS/OpenAI	MVP

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🛠️ Core Technologies

graph TD
    A[NLP] --> B((Chatbots))
    B --> C{Projects}
    C --> D[ClimateGuardian]
    C --> E[Coach CV]
    C --> F[DocuBot AWS]
    
    style A fill:#4CAF50,stroke:#388E3C
    style D fill:#2196F3,stroke:#1976D2
    style E fill:#FF9800,stroke:#F57C00
    style F fill:#9C27B0,stroke:#7B1FA2

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Common Stack:

• Language Processing: spaCy, NLTK, Transformers.
• Vector Storage: FAISS, ChromaDB.
• Frameworks: LangChain, Rasa.
• Infrastructure: Docker, AWS Lambda, Redis.

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🔍 Project Details

1. ClimateGuardian 🌍

Solution for Environmental NGOs

• Purpose: Climate monitoring + organizational collaboration.

Key Features:

• Interactive Dashboard: Real-time climate data visualization.
• Automated Alerts: Based on configurable thresholds.
• IoT Integration: Connects with environmental sensors.

Commands to Run:

docker-compose build && docker-compose up -d

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2. Coach CV 📄

Intelligent Resume Optimizer

• Purpose: Analyze resumes and provide personalized recommendations.

Key Features:

• Industry Keyword Detection: Identifies relevant terms by domain.
• Structural Analysis: Highlights areas for improvement using AI.
• Actionable Suggestions: Generates quantifiable recommendations.

Commands to Run:

npm install && flask run

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3. DocuBot AWS 🤖

Technical Support Assistant for AWS

• Purpose: 24/7 support + evolving knowledge base.

Key Features:

• RAG (Retrieval-Augmented Generation): Combines search and generation for intelligent responses.
• Multi-Level Cache: Utilizes Redis and Memcached for fast retrieval.
• AWS Console Integration: Assists directly with AWS services.

Commands to Run:

pip install -r requirements.txt && python setup_rag.py

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🚀 Quick Start

Minimum Requirements:

hardware:
  cpu: 4 cores
  ram: 8GB
  storage: 40GB SSD

software:
  docker: 20.10+
  python: 3.9+
  node: 16.x

Initial Setup Steps:

1. Clone the repository:

   git clone https://github.com/fdaniel-alvarez-dev/chatbot-suite.git

2. Set up the environment:

   cd chatbot-suite && make install-dependencies

3. Start base services:

   docker-compose up -d postgres redis

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📄 License

Licensed under the MIT License. See the LICENSE file for more details.

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🤝 Contribution

Contributions are welcome! Follow these steps:

1. Fork the repository.
2. Create a feature branch:

   git checkout -b feature/new-feature

3. Commit your changes:

   git commit -am "Add amazing feature"

4. Push to the branch:

   git push origin feature/new-feature

5. Open a Pull Request.

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