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| title: ERP-DocIQ | |
| emoji: 📄 | |
| colorFrom: indigo | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: 6.9.0 | |
| app_file: gradio_app.py | |
| pinned: false | |
| license: mit | |
| short_description: OCR/IDP + ERP NLQ chatbot on small models (MiniCPM) | |
| tags: | |
| - build-small-hackathon | |
| - backyard-ai | |
| - best-minicpm-build | |
| - best-agent | |
| - tiny-titan | |
| - minicpm | |
| - ocr | |
| - idp | |
| - nlq | |
| - rag | |
| - agent | |
| - fine-tuning | |
| # ERP-DocIQ — back-office automation on small models | |
| > **Build Small Hackathon · Practical track (Backyard AI).** A useful, problem-solving app | |
| > that runs on hardware you own — built entirely on **open models ≤32B**, with **OpenBMB | |
| > MiniCPM** as the load-bearing model (and fine-tuned for the domain). | |
| ## 💡 The idea | |
| Retail back-offices drown in paperwork and report requests. **ERP-DocIQ** is an open-source, | |
| UiPath-style assistant that **reads your documents**, **answers questions about your ERP in | |
| plain English**, and **automates the boring clicks** — all on small models, no per-robot | |
| license, no data leaving your tenancy. | |
| ## 🧩 The business problem | |
| A retail brand's IT team runs **UiPath** for invoice/PO processing and portal automation. It's | |
| **expensive** (per-robot / AI-Unit licensing, six figures/yr at volume), **brittle** (selector | |
| recorders break on every UI change), **locked-in** (closed IDP models you can't swap or | |
| fine-tune), and **slow to change** (new layouts wait on a closed retraining toolchain). On top | |
| of that, every "what did we spend in Q2?" becomes a **BI ticket**. They wanted an AI-native, | |
| open, cheaper alternative they control. | |
| ## 🛠️ The solution approach — one Gradio app | |
| 1. **Read any document (OCR + IDP).** A hybrid pipeline — OCR → classify → extract → normalize → | |
| enrich (RAG) → validate → post / human-review — reads orders, receipts, invoices, contracts | |
| and complex multi-layer forms, **even messy scans/photos**, into structured ERP records. | |
| 2. **Ask your ERP reports (ERP DocIQ chatbot).** Natural-language **NLQ → SQL**, analytics, | |
| summaries and **"why"** reasoning over a simulated retail ERP (vendors · POs · invoices · GL · | |
| inventory · returns). Every figure comes from **real SQL over the data** — the model only | |
| *phrases* the answer, it never invents numbers. | |
| 3. **Automate the clicks (agentic browser).** A self-correcting, multi-step browser agent drives | |
| a portal (dashboard → Procurement → Create Order → read the complex form) — selector-free and | |
| self-healing, replacing fragile RPA recorders. | |
| ## 🤖 Models used — small (≤32B), the right one for each job | |
| Three labs, eight models, all under the cap. **MiniCPM is the core.** | |
| | Lab | Model | Params | Role in ERP-DocIQ | | |
| |---|---|---:|---| | |
| | **OpenBMB** | **MiniCPM-V-4.6** | 8B | **primary OCR + vision extraction** (reads messy/rotated/scanned docs → JSON) | | |
| | **OpenBMB** | MiniCPM-o-4.5 | 8B | alt omni VLM | | |
| | **OpenBMB** | **MiniCPM3-4B** | **4B** | **ERP reasoning · NLQ→SQL · summarization — and the fine-tune target** | | |
| | **Cohere** | Aya-Vision-8B / 32B | 8–32B | alt OCR / VQA backend (23 languages) | | |
| | **Cohere** | Command R7B | 7B | alt RAG · NLQ · grounded reasoning | | |
| | **Black Forest Labs** | FLUX.1 [dev]/[schnell] | 12B | image *generation* → synthetic OCR stress docs (honestly, not an OCR model) | | |
| A single ~8B MiniCPM-V powers OCR **and** vision extraction **and** the grounded chat phrasing — | |
| so the whole product runs on small, swappable, open weights. `GET /api/models` lists which are live. | |
| ## 🎯 Fine-tuning — adapting a small model to the ERP domain | |
| We fine-tune **OpenBMB MiniCPM3-4B (4B)** on an instruction dataset built from the ERP | |
| knowledgebase (`results/erp_sft.jsonl`): | |
| - **Production:** a LoRA (PEFT/TRL `SFTTrainer`) recipe — `scripts/finetune_erp.py --backend hf`. | |
| - **Offline CPU demo (runs anywhere):** trains the ERP NLQ-routing head on the same data with a | |
| real train/test split — **untrained 8.3% → fine-tuned 91.7% (+83 pts)**, 100% routed-SQL | |
| execution. See `results/erp_finetune_report.json`. | |
| ## 📈 Benefits / value delivered | |
| - **Significantly lower inference cost** vs per-document RPA licensing (model routing + prompt caching). | |
| - **No vendor lock-in, data stays on-prem** — open weights you can run, read and fine-tune. | |
| - **Self-service ERP analytics** (NLQ) deflects routine BI/report tickets off the queue. | |
| - **Reads documents classic OCR can't** — MiniCPM-V cuts character error **~5.6× vs Tesseract** | |
| (CER **2.6%** vs 14.7%; see `results/ocr_quality_report.json`); field-exact **90.7%**. | |
| - **Honest, measured** — every claim is backed by a published eval/quality/fine-tune report. | |
| ## ✅ How it meets the Build Small criteria | |
| - **≤32B params** — every model is ≤32B; the reasoning/NLQ engine + fine-tune target is **4B**. | |
| - **Best MiniCPM Build** — MiniCPM-V (OCR + vision) and **MiniCPM3-4B** (reasoning/NLQ + | |
| fine-tuned) are the core of the experience; vision/omni variants qualify. | |
| - **Best Agent** — multi-step, self-healing agentic browser automation + an IDP state graph. | |
| - **Ships as a Gradio Space** in the Build Small org; runs offline (deterministic ERP engine + | |
| sidecar OCR) and upgrades to hosted MiniCPM when keys are set. | |
| ## ▶️ Use it (tabs) | |
| - **Process a document** — pick an OCR backend (`auto`, `minicpm`, `tesseract`) + a sample | |
| (try `extreme_receipt_photo` or `complex_invoice_messy`) or upload your own → multi-layer fields + KPIs. | |
| - **ERP DocIQ (chat)** — ask *"Why did spend rise in Q2 2026?"*, *"Top vendors by spend"*, | |
| *"late-payment rate"* → grounded answer + SQL + the fine-tuning panel. | |
| - **Search (RAG)** — semantic vendor-master retrieval. **Web Automation** — multi-step browser flow. | |
| ## 📦 Published results (`results/`) | |
| - `ocr_quality_report.json` — OCR CER/WER + field accuracy per backend. | |
| - `erp_finetune_report.json` + `erp_sft.jsonl` — fine-tune metrics + the instruction dataset. | |
| ## ⚙️ Configure (Space → Settings → Variables and secrets) | |
| - Variables: `MINICPM_BASE_URL=https://api.modelbest.cn/v1`, `MINICPM_MODEL=MiniCPM-V-4.6-Instruct` | |
| - Secret: `MINICPM_API_KEY=…` (OpenBMB/ModelBest). Tesseract ships via `packages.txt`. | |
| - **Runs fully offline without a key** — ERP DocIQ uses its deterministic SQL engine and OCR | |
| falls back to the sidecar, so every tab works. | |
| ## 🎥 Demo & social | |
| - **Demo video:** https://youtu.be/mWs7eRVH_GM | |
| - **Social post:** https://www.linkedin.com/posts/kaniskamandal_huggingface-buildsmall-buildsmall-share-7472163579094401024-fjp9/ | |
| - **Blog post:** https://huggingface.co/spaces/build-small-hackathon/ERP-DocIQ/blob/main/BLOG.md | |