Boost Vision LLM Accuracy with OCR Text Integration

I show an interesting approach where I send both an image and OCR text to a Vision LLM. The prompt is constructed to instruct the Vision LLM to prioritize the OCR text. This allows the use of a Vision LLM for structured output construction while relying on external OCR text, giving you more control over the results.

 

Solving Vision LLM Number Formatting Issues Using PaddleOCR and Sparrow

Discover how to fix number formatting errors in vision LLMs like Mistral! In this video, I show how Mistral misreads "56,000" as "56000" and how combining PaddleOCR’s text extraction with Sparrow’s spatial data processing solves this hallucination issue.

 

PaddleOCR 3.0: Supercharge Your AI

I upgraded to PaddleOCR 3.0 and explain the new PaddleOCR API integration. My goal is to integrate OCR result output with Vision LLM processing to enhance large-scale, structured table data output. 

 

Box Annotations in Sparrow for Structured Data Extraction

Check out my video on Box Annotations in Sparrow for Structured Data Extraction! I’ll show you how the Qwen2.5 vision model pulls bounding box annotations from images based on what you need. Plus, create simple descriptions and confidence score boxes. 

 

Structured Data Annotation with Qwen2.5 VL and MLX-VLM

Qwen2.5 VL can provide bounding box coordinates and confidence values for extracted structured data. This is useful for visual data review and reporting. I will explain with a practical example what prompt should be used to ensure Qwen2.5 returns this data. 

 

LLM Microservice with Instruction Calling

I describe the idea of implementing interaction with LLM through a concept of microservice with instruction calling. This works great for enterprise application use cases, such as data validation, workflor decisions.

 

Local LLM Instruction Processing with Sparrow

I explain how to execute instructions with a payload using a local LLM. This is useful when you want to process your data with an LLM and provide contextual instructions, specifying the desired outcome of what needs to be achieved. 

 

Vision LLM on Mac Mini M4 Pro: Real-World MLX Performance

I discuss the real-world MLX performance of Sparrow for structured data extraction with public access. The current Sparrow online instance runs on a Mac Mini M4 Pro with 64GB of memory. On average, it processes one page in 100 seconds. I explain why tokens-per-second measurements can be misleading when evaluating structured data extraction. 

 

Running Vision Models on Apple Silicon with MLX-VLM

I show and explain how to run Qwen and Mistral vision models on Apple Silicon with MLX-VLM. I share technical tips about how to run both models and show how to pass query prompt.

 

Dashboard with Gradio Python

This video showcases the Sparrow dashboard, where you can view statistics on document data extraction events processed by Sparrow. This elegant dashboard is built with Python using Gradio, a server-side web UI framework.

 

Extract Structured Data from Documents with Sparrow (Free Tier Available)

I built Sparrow for document data extraction 🚀 It's fully open-source and runs locally on your machine You can extract structured data from any document using powerful Mistral 24B 8bit and Qwen 2.5 72B 4bit models It's free to try with no registration (3 calls per 6 hours, max 3-page documents) and doesn't send your documents to third parties 

 

Oracle DB 23ai Free Connection Pool in Python

I describe how to connect to Oracle DB from Python. I explain why DB connection pool is important for better performance. Connection is done through thin oracledb mode, without installing Oracle Client.

 

Temporary Files Cleaner for Gradio Web App

Learn how to implement an automatic temporary file cleanup solution for Gradio web applications. This tutorial shows you how to prevent disk space issues by periodically removing old upload files and folders that Gradio leaves behind. Perfect for developers who deploy Gradio apps in production environments or run memory-intensive applications. 

 

Building AI Agent for Local Structured JSON Output

I explain key steps of building AI agent to process document and extract structured JSON data locally. I'm running it with Sparrow and using Qwen VL model for vision processing backend and OCR. The steps are explained with Sparrow code walkthrough. 

 

Querying Non Existing Fields with Qwen2.5 Vision LLM

I describe how Sparrow helps to query non existing fields with Qwen2.5 Vision LLM. Running it locally with MLX and MLX-VLM. 

 

Structured Data Extraction with Sparrow Agent: Vision LLM & Prefect in Action

Discover how to streamline your data extraction process with Sparrow Agent! In this tutorial, I showcase how Sparrow Agent leverages Vision LLM to intelligently handle complex data tasks, while Prefect ensures every step is logged and monitored for maximum transparency and efficiency. Join me as I break down the process and share tips for optimizing your automated workflows. 

 

Building Web UI Apps with Python Gradio – A Java Developer’s Perspective

I explain building Web UI apps with Python Gradio framework. I used to work with Java in the past and was building enterprise Web UI apps with JSF. Based on this experience I can tell, Gradio is awesome framework for server side generated UI - it is easy to define UI components and control UI flow with event triggers.

 

Improving Qwen-VL Structured Output with Image Cropping

Explaining how I'm improving structured output results from Qwen-VL with image cropping in Sparrow.

 

 

Apple MLX Vision LLM Server with Ngrok, FastAPI and Sparrow

I show how I run Apple MLX backend on my local Mac Mini M4 Pro 64GB and access it from the Web through Ngrok, with automatically provisioned HTTPS certificate. 

 

Vision LLM Structured Output with Sparrow

I show how Sparrow UI Shell works with both image and PDF docs to process and extract structured data with Vision LLM (Qwen2) in the MLX backend.