## 👀 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.
---
## 🤗 Find our model here
## 🚀 Installation and inference
```bash
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.
---
## 📥 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:
```json
{
"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:
```bash
python -m pip install '.[mcp]'
```
Configure your MCP client to launch this stdio bridge:
```bash
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](https://github.com/SoMarkAI/docision/blob/main/docs/python-package.md#mcp) for prerequisites and remote GPU connections.
