Mistral OCR 4 + Sparrow: Document to JSON

Follow-up to the Mistral OCR + Sparrow integration video. Mistral released OCR 4 — the latest model with improved accuracy, native bounding box extraction, and structural block labels. One model string change in Sparrow to pick it up.

In this video, extracting a long financial statement table into structured JSON using Mistral OCR 4 + Mistral Small 4.

Sparrow is open source and local-first by design — documents never leave your infrastructure unless you choose the cloud backend.

Mistral OCR + Sparrow: Document to JSON

Integrated Mistral OCR as a new cloud inference backend into Sparrow, an open-source document extraction platform. This gives Sparrow a full cloud option alongside its existing local backends (MLX, vLLM), so users without GPU infrastructure can still run enterprise-grade document extraction.

Pipeline: Mistral OCR converts the document to structured HTML, then Mistral Small extracts and transforms the data into JSON based on a defined schema with field-level hints.

In this video, extracting a bonds portfolio table with hint-driven rules:

• Instrument name normalization (extracting issuer brand from full fund names)
• European number formatting (period as thousands separator, comma as decimal)
• Percentage formatting with sign preservation
• Derived risk classification computed from profit/loss percentage

Same Sparrow API, same schema and hint format as local backends — just switch the backend flag to run on Mistral Cloud instead of MLX or vLLM.

Sparrow is open source and local-first by design — documents never leave your infrastructure unless you choose the cloud backend.

⭐ GitHub: github.com/katanaml/sparrow
🌐 Live demo: sparrow.katanaml.io 

 

New Ministral 3 14B vs Mistral Small 3.2 24B Review

I review data accuracy retrieval and inference speed for the new Ministral 3 14B model vs older Mistral Small 3.2 24B. Older and larger 24B model wins this time. 

 

Invoice Data Processing with Mistral LLM on Local CPU

I explain the solution to extract invoice document fields with open-source LLM Mistral. It runs on CPU and doesn't require Cloud machine. I'm using Mistral 7B LLM model, Langchain, Ctransformers and Faiss vector store to run it on a local CPU machine. This approach gives a great advantage for enterprise systems, when running ML models on Cloud is not allowed for privacy reasons.