SoMarkAI/Docision-1.0

Jev-Inspired Multimodal Decisions for Documents.

👀 Decision intelligence beyond text

One document can answer multiple decision questions. Given the same invoice, a finance team asks whether it is eligible for reimbursement, an archive system asks how to classify it, and a reviewer checks whether signatures and seals are present. There is one document, but more than one decision.

Docision brings candidate-answer decisions to multimodal documents: from document classification and visual attribute recognition to understanding content relationships, applying business rules, and even analyzing engineering drawings and scientific charts. Docision unifies document tasks through one simple interaction: Your Document. Your Question. Your Decision.

Docision focuses on document understanding and decisions. Identify document categories, check relationships between fields, verify completeness, and make decisions or route documents under the rules you provide.

• ⚡ Keep the context: multiple questions about the same image benefit from a multiplicative speedup.
• 🧩 Bring your choices: define candidates independently for each question; both their content and count can vary.
• 🔎 Inspect each decision: receive the model's choice and the probability of each candidate.

If this project helps you, please give it a ⭐ Star in the top right—it is the greatest encouragement for the developers!

🎯 Document decisions beyond a 27B model—higher speed, higher accuracy.

Covers document understanding and decisions, and cross-industry classification, with higher speed and quality.

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🤗 Find our model here

🚀 Installation and inference

git clone https://github.com/SoMarkAI/docision.git
cd docision
python3 -m venv .venv
source .venv/bin/activate
python -m pip install -e '.' huggingface_hub
hf download SoMarkAI/Docision-1.0 --local-dir models/Docision-1.0

CUDA_VISIBLE_DEVICES=0 python demo_docision.py \
  --model models/Docision-1.0 \
  --input examples/multiple.json \
  --output outputs/document-decisions.json

Results are saved to outputs/document-decisions.json. Replace the input file to try your own documents.

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📥 Input and output

One JSON file describes a document image and its questions. The image path is resolved relative to the JSON file; each question defines its own question text and candidates:

{
  "image": "page.png",
  "questions": [
    {
      "id": "q1",
      "question": "Which category does this document belong to?",
      "candidates": {"A": "Invoice", "B": "Receipt", "C": "Contract"}
    }
  ]
}

Results preserve question order, including each candidate's probability and the final choice in decision.

Candidates contain only label, text and probability; decision reports the selected candidate, its index and tie-handling information.

🔌 MCP integration

Connect Docision to Codex or Claude Code to inspect local documents, ask multiple questions, and export JSON or CSV results.

Install MCP dependencies from the repository:

python -m pip install '.[mcp]'

Configure your MCP client to launch this stdio bridge:

docision mcp --api-url http://127.0.0.1:7860 \
  --token-file /absolute/path/to/api-token \
  --allowed-root /absolute/path/to/documents \
  --output-dir /absolute/path/to/exports

The bridge connects to a running Docision model service. Document and export paths belong to the MCP host; the model runs on the service host. See the MCP setup instructions for prerequisites and remote GPU connections.

This project is licensed under the Apache License 2.0.