allyoucanrag 0.1.1

A reusable RAG package and optional Streamlit app for academic question answering.

allyoucanRAG

This repository contains the allyoucanrag package, published under the public-facing brand name allyoucanRAG. The RAG core is packaged as a reusable Python library, while the Streamlit UI remains available as an optional frontend.

What the project provides

• The allyoucanRAG package for reusable academic RAG workflows.
• A Streamlit QA app for course-material and user-uploaded PDF retrieval.
• A dual-vector-store RAG workflow based on Chroma and LangChain.
• Runtime-managed data directories for uploads, vector stores, and local config.

Important packaging note

The following runtime data is intentionally excluded from package distributions:

• CourseMaterials/
• UserUploads/
• chroma_db/
• config.json
• .streamlit/secrets.toml

This means your course materials are treated as external runtime data, not packaged assets. That matches your PyPI goal.

Current layout

MAC-Academy-QA-System/
├── app.py
├── pyproject.toml
├── MANIFEST.in
├── requirements.txt
├── config.example.json
├── src/
│   └── allyoucanrag/
│       ├── __init__.py
│       ├── batch_upload_helper.py
│       ├── config.py
│       ├── document_manager.py
│       ├── exceptions.py
│       ├── paths.py
│       ├── rag_system.py
│       ├── streamlit_app.py
│       └── utils.py
└── CourseMaterials/

Installation

Local development

pip install -r requirements.txt

Install the package only

pip install .

Install the package with the Streamlit frontend

pip install ".[streamlit]"

After you publish to PyPI, the install command will become:

pip install "allyoucanrag[streamlit]"

The public-facing brand is allyoucanRAG. The PyPI distribution name and the Python import path are both allyoucanrag.

CLI usage

After installing the package, start the interactive CLI with:

allyoucanrag start

The CLI can update API keys, upload supported text documents, and ask questions against the knowledge base.

Configuration

The package resolves API keys in this order:

1. Streamlit secrets
2. Environment variables
3. config.json

Example local config.json:

{
  "OpenAIAPIKey": "your-openai-api-key",
  "LangChainAPIKey": "your-langchain-api-key"
}

Supported environment variables:

• OPENAI_API_KEY
• LANGCHAIN_API_KEY
• MAC_ACADEMY_QA_HOME

If MAC_ACADEMY_QA_HOME is set, runtime directories are resolved from that base path. Otherwise the current working directory is used.

Run the Streamlit app

streamlit run app.py

Use as a package

from allyoucanrag import DualVectorStoreRAG, resolve_runtime_paths

runtime_paths = resolve_runtime_paths(base_docs_dir="D:/external-course-materials")
rag = DualVectorStoreRAG(runtime_paths=runtime_paths)
rag.initialize_base_vectorstore()
rag.initialize_user_vectorstore()

Runtime data directories

By default, the package expects these runtime paths under the active base directory:

• CourseMaterials/
• UserUploads/
• chroma_db/base/
• chroma_db/user/
• config.json

These are not installed into site-packages and should be managed by the runtime environment.

Build distributions

python -m build --sdist --wheel

The generated files appear in dist/.

Publish checklist

1. Choose and add a real LICENSE file.
2. Verify README.md renders correctly on PyPI.
3. Build with python -m build --sdist --wheel.
4. Upload to TestPyPI first.
5. Upload to PyPI.

Detailed release steps are in DEPLOYMENT.md.