Sparrow Standard Mode: Document Extraction with Ministral 14B

A look at Sparrow's Standard tier, powered by Ministral 3 14B via MLX-VLM.

In this video I run a document through Sparrow's UI (sparrow.katanaml.io/process) using the Standard model — Ministral 14B. It extracts structured line-item data into clean, valid JSON in about 22 seconds, no schema drift, no formatting issues.

Ministral 14B is the default model behind Sparrow's Standard tier — built for fast, everyday document extraction with strong accuracy, running fully local with no cloud dependencies.

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.

Building an AI Agent That Searches the Web and Makes Investment Decisions

In this video I build a local agentic AI pipeline that analyzes a bond portfolio and makes sell/hold decisions based on risk analysis and live web search data.

The agent runs four steps: load portfolio positions from JSON, classify each position as low/medium/high risk, search the web per position via Tavily API for historical performance and current outlook, then make a final sell/hold decision with reasoning — all powered by Gemma 4 31B running locally on Apple Silicon via mlx-vlm. No data leaves your machine.

All steps orchestrated with Prefect.

🔗 GitHub: https://github.com/katanaml/sparrow
🌐 Live: https://sparrow.katanaml.io
📧 Enterprise inquiries: abaranovskis@redsamuraiconsulting.com

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 

 

Sparrow 0.6.0: New Production-Ready UI for Local Document AI

Sparrow just got a complete UI overhaul — rebuilt from the ground up with Next.js and shadcn for a production-grade experience.

What's new in this release:

- Faster document upload and extraction workflow

- Real-time analytics dashboard with usage metrics, model distribution, and geographical reach

- Built-in feedback collection

- Dark mode support Fully responsive mobile layout 

Sparrow remains fully local — your documents are processed on-device with Vision LLMs, with nothing stored on disk and no cloud dependencies.

Gemma 4 12B vs Ministral 14B: Who Wins at Structured Table Extraction?

Head-to-head test: Gemma 4 12B vs Ministral 14B on structured table extraction.

In this video, I run a head-to-head test: Gemma 4 12B (8-bit and bf16) vs Ministral 14B (8-bit), extracting data from a 5-row table — two columns, JSON schema, array output.

Results:

• Gemma 4 12B (both quantizations): fails to return a proper JSON array
• Ministral 14B 8-bit: extracts all rows correctly

Building Agentic AI Pipelines for Document Analysis

In this video, I show how to build a local agentic AI pipeline using Sparrow to extract and analyze data from financial documents. 

 The agent runs two steps: 

- Extract structured data from a bonds table image using Sparrow Parse pipeline and Ministral 3B 14B model 

- Analyze portfolio risk using Sparrow Instructor pipeline and Gemma 4 31B model — classifying each position as low, medium, or high risk

 

Both steps run as Prefect tasks inside a single flow, fully locally — no data leaves your machine.