Sparrow Parse Vision LLM FastAPI Endpoint

Sparrow provides an API for accessing the Sparrow Parse agent, allowing you to run document extraction workflows directly from your existing systems. It helps simplify how data is pulled from documents and integrated into your workflows. 

 

Sparrow Parse Invoice Query with Vision LLM

New Sparrow Agent - Sparrow Parse, works with Qwen2 Vision LLM. 

What it does: 

1. Accepts query with JSON schema, this helps to solve few things at once - provides JSON structure for LLM to generate response, and hints LLM what types to use for each response element 

2. Runs inference on your GPU of choice, either cloud or local GPU 

3. Validates JSON response, based on query schema

Running Qwen2 Vision LLM on Hugging Face ZeroGPU API

Explaining my experience running Sparrow Parse with Qwen2 Vision LLM inference on Hugging Face ZeroGPU instance. 

 

Document Querying with Qwen2-VL-7B and JSON Output

In this video, I demonstrate how to perform document queries using Qwen2-VL-7B. By simplifying field names, we streamline the prompts, making them more efficient and reusable across different documents. This approach is similar to running SQL queries on a database, but tailored for language models like Qwen2-VL-7B, with results returned in JSON format. 

 

Table Parsing with Qwen2-VL-7B

I show how to retrieve structured JSON output from table image using Qwen2-VL-7B. This VLLM performs OCR and data mapping tasks all out of the box, also it can return structured JSON output without use of intermediate frameworks. 

 

Sparrow Parse: Table Data Extraction with Table Transformer and OCR

I explain how we extract data with Sparrow Parse, using Table Transformer to identify table area and build table structure to be processed by OCR. Sparrow Parse implements additional logic to clear-up and improve (removing noise, merging columns, adjusting rows) table structure generated by Table Transformer.

 

Table Header Extraction with Table Transformer

Table Transformer model is able to provide table functional analysis. As result we can identify table header area and build cells to enclose each column header. In the next step with crop each cell and read data with OCR. Finally we get structured data for table header column names.