Here's a professional, well-structured README.md for your project that utilizes Camel-AI, OpenAI, and Ollama to select and rank the best LLM for user tasks.

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🧠 Task-Aware LLM Router using Camel-AI, OpenAI, and Ollama

A unified AI orchestration framework that selects the best large language model (LLM) for a given user task using Camel-AI, OpenAI API, and Ollama. It evaluates, ranks, and logs LLM performance over time to dynamically improve routing for future tasks.

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🚀 Features

• ✅ Dynamic Task Understanding: Automatically identifies task type (e.g., summarization, coding, translation).
• 🧠 Model Selection & Routing: Uses Camel-AI to choose between models from OpenAI, Ollama, and others.
• 📊 Ranking & Feedback Loop: Tracks model performance per task type to rank them for future requests.
• ⚙️ Plug-and-Play Model Management: Seamlessly integrates local (Ollama) and cloud-based models (OpenAI).
• 🔁 Self-Optimizing: Learns which model performs best for specific task categories over time.

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🏗️ Architecture Overview

User Task
   │
   └─► Task Analyzer (Camel-AI)
          │
          ├─► Model Selector
          │     ├─ OpenAI GPT-4 / GPT-4o
          │     ├─ Ollama (Mistral, LLaMA, etc.)
          │     └─ Other plug-in models
          │
          └─► Execution Engine → Response
                          │
                          └─► Ranking & Logging Module

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📦 Tech Stack

• Camel-AI – Agent framework for task decomposition and model orchestration.
• OpenAI API – Access to top-tier GPT models.
• Ollama – Local model runner for LLaMA, Mistral, etc.
• LangChain (optional) – Chaining tasks and memory.
• Python – Core implementation.

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🔧 Setup Instructions

1. Clone the Repository

git clone https://github.com/Dhiraj309/Smart-AI-Agent.git
cd task_classifier_project

2. Install Dependencies

pip install -r requirements.txt

3. Start Ollama and Download Models

ollama serve
ollama pull mistral
ollama pull llama3

4. Set Environment Variables

Create a .env file:

OPENAI_API_KEY=your-openai-key

5. Run the Application

python main.py

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🧪 Example

from router import route_task

result = route_task("Translate this English paragraph to French.")
print(result)

Sample output:

{
  "best_model": "openai:gpt-4o",
  "response": "Voici le paragraphe traduit...",
  "alternatives": {
    "ollama:mistral": "Voici...",
    "ollama:llama3": "Ceci est..."
  },
  "ranking_log": "Logged performance and accuracy score."
}

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📈 Model Ranking Strategy

• Metrics include:

  • Accuracy (via eval benchmarks)
  • Response time
  • User feedback (thumbs up/down)

• Rankings stored in a local DB (sqlite, json, or Postgres)

• Adaptive learning: Next task considers ranking history.

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📁 Project Structure

│   benchmark_summary.csv
│   Llama3ModelFile
│   main.py
│   requirements.txt
│   setup_llama3.sh
│   
├───.ipynb_checkpoints
│       benchmark_log-checkpoint.jsonl
│       benchmark_summary-checkpoint.csv
│       Llama3ModelFile-checkpoint
│       main-checkpoint.py
│       setup_llama3-checkpoint.sh
│
├───agents
│   │   critic_agent.py
│   │   judge_agent.py
│   │   task_agent.py
│   │
│   └───.ipynb_checkpoints
│           critic_agent-checkpoint.py
│           judge_agent-checkpoint.py
│           task_agent-checkpoint.py
│
├───app
│       chat_app.py
│       pipeline.py
│
├───benchmark
│   │   evaluator.py
│   │
│   └───.ipynb_checkpoints
│           evaluator-checkpoint.py
│
├───models
│   │   runner.py
│   │
│   └───.ipynb_checkpoints
│           runner-checkpoint.py
│
└───utils
|    │   benchmark_utils.py
|   │   logger.py
|    │
|    └───.ipynb_checkpoints
|            logger-checkpoint.py
└── README.md

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✅ Future Improvements

• Add LangChain-style memory and retrieval
• Web UI dashboard for ranking visualization
• Fine-tuning feedback weights per user
• Model cost analysis for budget-sensitive routing

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🤝 Contributing

Pull requests are welcome! For major changes, open an issue first to discuss.

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📜 License

MIT License