local-ai-infra-generation 1.0.2

A tool to generate infrastructure files (Dockerfile, etc.) for codebases using local LLMs.

local-AI-infra-generation

local-AI-infra-generation leverages local Large Language Models (LLMs) to analyze code repositories and automatically generate infrastructure files such as Dockerfiles and docker-compose.yml. All processing is performed locally, ensuring privacy and control over your codebase.

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Features

• Codebase Embedding: Index and embed your codebase for semantic search and retrieval.
• Natural Language Q&A: Ask questions about your codebase and receive context-aware answers.
• Automated Infrastructure Generation: Generate Dockerfiles and docker-compose.yml files tailored to your project.
• Multi-language Support: Works with Python, JavaScript, TypeScript, and Go projects.

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Getting Started

1. Prerequisites

• Python 3.11+
  Ensure you have Python 3.11 or higher installed.
  Check with:

  python --version
  

• Ollama
  Download and install Ollama for local LLM inference.

• C/C++ Build Tools
  Required for building tree-sitter-languages.

• Git (optional, for cloning repositories)

2. Installation

a. Clone the Repository

git clone https://github.com/yourusername/local-AI-infra-generation.git
cd local-AI-infra-generation

b. Create a Virtual Environment

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

c. Install Dependencies

This project uses uv for fast dependency management, but you can use pip as well.

With uv:

uv pip install -r requirements.txt
uv pip install -e .

Or with pip:

pip install -r requirements.txt
pip install -e .

Alternatively, install via pyproject.toml:

pip install .

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Usage

1. Start Ollama

Make sure the Ollama server is running:

ollama serve

2. Run the CLI

uv run python -m src.main --help  

Common Commands

• Embed a Project:

  uv run python -m src.main embed /path/to/your/project
  uv run python -m src.main embed ../PlotTwister
  

• Ask a Question:

  uv run python -m src.main ask "How does authentication work?" --project your_project_name
  uv run python -m src.main ask "what does the main.py do" --project "PlotTwister"
  

• List Embedded Projects:

  uv run python -m src.main list
  

• Generate Dockerfile:

  uv run python -m src.main generate-docker --project your_project_name
  

generate full infra for a multi-service repo

uv run python -m src.main generate-infra /path/to/repo --output infra

generate just the Dockerfile for a single service folder

uv run python -m src.main generate-docker /path/to/repo/service

• Generate docker-compose.yml:

  uv run python -m src.main generate-compose --project your_project_name
  

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Configuration

Edit src/config.yaml to customize:

• Model names and versions
• Supported languages and file extensions
• ChromaDB storage directory
• Ollama server URL

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Dependencies

Key packages (see pyproject.toml for full list):

• chromadb
• langchain
• langchain-community
• pyyaml
• requests
• tqdm
• tree-sitter-languages
• typing-extensions

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Development

• All source code is in src/.
• Prompt templates are in prompt/.
• Data and ChromaDB indexes are stored in data/.

Running Tests

TODO: Add unit tests and instructions for running them.

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check db from chromadb:

uv run python -m src.chroma_manager --db_dir data/chroma_index --list

uv run python -m src.chroma_manager --db_dir data/chroma_index --preview PlotTwister

Troubleshooting

• Ollama not found:
  Ensure Ollama is installed and available in your PATH.

• tree-sitter language .so files missing:
  If you encounter errors about missing .so files, ensure tree-sitter-languages is installed and built correctly.

• Model download issues:
  The first run will download required models. Ensure you have a stable internet connection.

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TODO

• Add comprehensive unit and integration tests.
• Improve error handling and user feedback.
• Add support for more programming languages (e.g., Java, Rust).
• Enhance prompt templates for better infrastructure generation.
• Add web or GUI interface.
• Document API for programmatic usage.
• Support for private model registries and custom LLMs.
• Optimize embedding and retrieval for large codebases.
• Add CI/CD pipeline for automated testing and deployment.
• Generate terraforms artifacts.

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License

This project is licensed under the Apache 2.0 License.

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Acknowledgements

• Ollama
• LangChain
• ChromaDB
• tree-sitter

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Contact

For questions or contributions, please open an issue or pull request on GitHub.