thanhtunguet/python-hyper-pack-base repository overview

⁠Python Hyper Pack Base

This repository contains the Dockerfile and configuration to build a comprehensive Python base image, equipped with dependencies for AI agents, graph document embedding, vector databases, and deep learning.

⁠Overview

The purpose of this base image is to provide a pre-configured environment with heavy dependencies pre-installed. This accelerates the build time for downstream applications that rely on these libraries, by caching the installation of large packages.

Key components included:

• Python 3.12 base environment.
• Graph & Vector DB: Neo4j, Qdrant client.
• Machine Learning & NLP: Torch, Transformers, Sentence Transformers, Spacy.
• Document Parsers: Docling, PyPDF, Python-docx.
• Code Parsing: Tree-sitter and various language packs.
• API & Web: FastAPI, Uvicorn, Requests.
• Agent SDKs: GitHub Copilot SDK, FastMCP.

⁠CI/CD Pipeline

This repository includes a CircleCI configuration (.circleci/config.yml) that automatically builds and pushes the Docker image to Docker Hub whenever changes are merged into the main or master branches.

⁠CircleCI Environment Variables

To allow the CircleCI pipeline to push to Docker Hub, the following environment variables must be configured in your CircleCI project settings or context:

• DOCKERHUB_USERNAME: Your Docker Hub username.
• DOCKERHUB_TOKEN: A Docker Hub Personal Access Token (PAT) with push access.

⁠Usage

You can use the built image as a base for your other projects by specifying it in their Dockerfile:

FROM <your-dockerhub-username>/python-hyper-pack-base:latest

# Your application setup here...

⁠Local Development

If you want to build the image locally to test changes:

docker build -t python-hyper-pack-base:latest .