BUILD.md

COGENBAI Build Guide

This guide explains how to build, deploy, and use COGENBAI from source, including Ollama integration.

Prerequisites

• Python 3.8 or higher
• CUDA-capable GPU (recommended)
• Git
• Docker (optional)
• Ollama

Local Development Setup

1. Clone the repository:

git clone https://github.com/algoscienceacademy/cogenbai.git
cd cogenbai

2. Create a virtual environment:

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

3. Install dependencies:

pip install -e ".[dev]"

Building the Model

1. Download the base model:

python scripts/download_model.py --model codegen-16B-multi

2. Train or fine-tune (optional):

python scripts/train.py \
    --model-path models/codegen-16B-multi \
    --train-data data/code_samples \
    --epochs 3

Ollama Integration

1. Install Ollama:

curl -fsSL https://ollama.com/install.sh | sh

2. Create Modelfile:

# Create Modelfile
FROM codellama
PARAMETER temperature 0.7
PARAMETER top_p 0.95
SYSTEM """
You are COGENBAI, an advanced code generation AI created by Algo Science Academy.
Created by: Shahrear Hossain Shawon
Organization: Algo Science Academy
"""

# Build the model
ollama create cogenbai -f Modelfile

3. Deploy with Ollama:

ollama run cogenbai

Building with Ollama

Prerequisites

• Ollama installed on your system
• Base model files ready

Steps to Build Model in Ollama

1. Create a Modelfile:

# Modelfile
FROM codellama
PARAMETER temperature 0.7
PARAMETER top_p 0.95
PARAMETER num_ctx 4096

# Model configuration
SYSTEM """
You are COGENBAI, an advanced code generation AI.
Focus: Code generation and software development assistance
Created by: Shahrear Hossain Shawon
Organization: Algo Science Academy
"""

# Include base model files
FROM models/codegen-16B-multi

2. Build the model in Ollama:

# Navigate to project directory
cd cogenbai

# Build the model
ollama create cogenbai -f Modelfile

# Verify the build
ollama list

3. Run the model:

ollama run cogenbai

Testing the Build

Test your model with a simple prompt:

ollama run cogenbai "Write a Python function to calculate fibonacci sequence"

Troubleshooting Ollama Build

If you encounter issues:

1. Check Ollama logs:

ollama logs

2. Rebuild model if needed:

ollama rm cogenbai
ollama create cogenbai -f Modelfile

Docker Deployment

1. Build Docker image:

docker build -t cogenbai:latest .

2. Run container:

docker run -d -p 8000:8000 cogenbai:latest

Project Structure

cogenbai/
├── cogenbai/
│   ├── core/           # Core model implementation
│   ├── languages/      # Language-specific generators
│   ├── templates/      # Code templates
│   ├── collaboration/  # Real-time collaboration
│   ├── review/        # Code review tools
│   ├── testing/       # Test generation
│   └── api/           # REST API
├── tests/             # Unit and integration tests
├── scripts/           # Build and utility scripts
└── docs/             # Documentation

Configuration

1. Create configuration file:

# config.yaml
model:
  name: codegen-16B-multi
  device: cuda
  max_length: 1024
  temperature: 0.7

language:
  default: python
  style:
    python: black
    javascript: prettier

2. Apply configuration:

from cogenbai import CogenConfig
config = CogenConfig.load('config.yaml')

API Deployment

1. Start the API server:

uvicorn cogenbai.api.server:app --host 0.0.0.0 --port 8000

2. Access API documentation:

http://localhost:8000/docs

Testing

Run the test suite:

pytest tests/

Development Workflow

1. Create new feature branch:

git checkout -b feature/new-feature

2. Make changes and run tests:

pytest tests/
black cogenbai/

3. Build documentation:

mkdocs build

Performance Optimization

1. Enable CUDA acceleration:

model = CogenBAI(device="cuda")

2. Batch processing:

config = CogenConfig(batch_size=4, num_workers=2)

Monitoring

1. Start Prometheus metrics:

docker-compose up -d prometheus grafana

2. Access dashboard:

http://localhost:3000

Troubleshooting

Common issues and solutions:

1. CUDA Out of Memory:

export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:128

2. Model Loading Issues:

import torch
torch.cuda.empty_cache()

Security Considerations

1. API Authentication:

from fastapi.security import OAuth2PasswordBearer
oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")

2. Rate Limiting:

from fastapi_limiter import FastAPILimiter
await FastAPILimiter.init(redis)

Production Deployment

1. Using Kubernetes:

kubectl apply -f k8s/

2. Load Balancing:

kubectl apply -f k8s/ingress.yaml

Contributing

1. Fork the repository
2. Create feature branch
3. Make changes
4. Submit pull request

Support

For support and questions:

• Email: contact@algoscienceacademy.com
• GitHub Issues: Create Issue

License

Copyright (c) 2024 Algo Science Academy. All rights reserved.