docai-toolkit 0.1.0

Local OCR + Markdown + RAG toolkit with optional Hugging Face endpoints.

DocAI Toolkit

Local OCR + Markdown + RAG with optional Hugging Face/custom endpoints. Renamed to avoid PyPI name collisions (docai-toolkit package import is docai_toolkit).

• pdf_viewer_app.py: Tkinter UI to open PDFs, run OCR → Markdown, and “chat” via retrieval + generation.
• docai_toolkit/: library for OCR (local Tesseract or remote endpoint), embedding/indexing (local or remote), and simple chat over FAISS.
• Status: under active development; APIs and defaults may change as the AI ecosystem moves quickly.

Requirements

• Python 3.9+
• Runtime deps vary by script:
  • Viewer: PyPDF2, reportlab (for saving)
  • RAG scripts: langchain, langchain-community, transformers, accelerate, bitsandbytes, sentence_transformers

Install everything:

pip install -r requirements.txt
# or editable install
pip install -e .

Usage

GUI Viewer

python pdf_viewer_app.py

• Open: loads all pages of a PDF into the text area.
• Save As: renders the text area content into a new PDF (requires reportlab).
• OCR → Markdown: run OCR on a PDF and save Markdown to the configured output directory (local Tesseract or remote OCR endpoint via HF/custom).
• Chat: build a quick FAISS index over a chosen Markdown file and query it with a selected HF model (remote endpoint or local HF pipeline).
• Settings: set HF token, optional custom endpoints (OCR/embeddings/LLM), model choices, and output directory. Settings persist to ~/.docai/config.json. Env vars (HF_TOKEN, HUGGINGFACEHUB_API_TOKEN, DOC_AI_OUTPUT_DIR) are auto-read.

Hugging Face onboarding (fast path)

1. Create a Hugging Face access token: https://huggingface.co/settings/tokens (choose “Read” or “Write” as needed).
2. Export it so the app can auto-load it:

   export HF_TOKEN=your_token_here
   # or HUGGINGFACEHUB_API_TOKEN=your_token_here
   

3. Pick models (examples):
   • OCR: point the OCR endpoint at a hosted OCR model (HF Inference API URL).
   • Embeddings: e.g., sentence-transformers/all-mpnet-base-v2 via Inference Endpoints (text-embeddings task) or local.
   • LLM: e.g., mistralai/Mistral-7B-Instruct-v0.1 via Inference Endpoints or local HF pipeline.
4. Start the app, open Settings, and paste endpoints/models if you didn’t set env vars. Output dir can be set there as well.

Environment variables:

• HF_TOKEN / HUGGINGFACEHUB_API_TOKEN / DOC_AI_HF_TOKEN: auth token (auto-loads into LLM + embeddings).
• DOC_AI_OUTPUT_DIR: default output directory for OCR/Markdown.

Docker

Build:

docker build -t docai-toolkit .

Run (GUI requires X/Wayland forwarding; for headless tasks, override CMD):

docker run --rm -v $PWD:/data docai-toolkit python -m pytest -q
# or override to run OCR in batch using the library CLI you add

macOS GUI via XQuartz:

1. Install/start XQuartz (brew install --cask xquartz; enable “Allow connections from network clients” in prefs and restart).
2. Allow local clients: xhost +localhost
3. Run:

docker run --rm -it \
  -e DISPLAY=host.docker.internal:0 \
  -v /tmp/.X11-unix:/tmp/.X11-unix \
  docai-toolkit

For day-to-day use, running natively is simpler; use the container when you need an isolated, reproducible environment.

Tests

Basic round-trip test for the viewer’s PDF writer:

pytest

reportlab must be installed for the test to run.

OCR + RAG (docai_toolkit/)

• OCR: pluggable clients (RemoteOcrClient for HF/custom endpoints, TesseractOcrClient local fallback) that turn PDFs into Markdown (ocr/pipeline.py).
• RAG: build a FAISS index from Markdown (rag/index.py), then chat using a chosen HF model (rag/chat.py).
• Config: lightweight dataclasses in docai_toolkit/config.py for selecting providers/models; saved at ~/.docai/config.json.
• Remote-friendly: use HF token + model ids by default; configs allow custom OCR/embedding/generation endpoints. FAISS runs locally for fast retrieval.

To experiment locally:

# OCR to Markdown (Tesseract fallback requires pytesseract + pdf2image installed)
python - <<'PY'
from pathlib import Path
from docai_toolkit.ocr import TesseractOcrClient, run_ocr_to_markdown
client = TesseractOcrClient()
md_path = run_ocr_to_markdown(Path("your.pdf"), Path("outputs"), client)
print("Saved:", md_path)
PY

# Build index + chat (requires sentence_transformers + transformers)
python - <<'PY'
from pathlib import Path
from docai_toolkit.rag import build_index_from_markdown, chat_over_corpus, load_index
index_path = Path("outputs/faiss_index")
db = build_index_from_markdown([Path("outputs/your.md")], persist_path=index_path)
print(chat_over_corpus(db, "What is this document about?", model_id="mistralai/Mistral-7B-Instruct-v0.1"))
# Later: db = load_index(index_path)
PY

License

CC BY-NC-SA 4.0 (see LICENSE).