Instruction-Based Data Analysis with Sparrow and Local LLM

In this video, I show how to use Sparrow instruction processing pipeline to analyze a bond portfolio JSON extracted from a financial document — all running locally, no external APIs.

I run three different analysis cases using Gemma 4 31B on Apple Silicon Mac Mini M4 Pro:

• Risk classification — categorize each position into low, medium, or high risk based on loss percentage
• Concentration risk — flag overweight positions above 20% portfolio weighting
• Portfolio aggregation — total valuation, weighted average P&L, best and worst performer

All three cases use the same sparrow-instructor pipeline, demonstrating how different instruction types — classification, rule-based flagging, and aggregation — are handled by a single local LLM.

My Experience with PyCharm AI Assistant

Explaining my experience with PyCharm AI Assistant. Showing example how code changes can be reviewed one by one, before they are accepted into your codebase.

 

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. 

 

Extracting Financial Market Stock Data from Images with Vision LLM

In this video, I demonstrate how to extract financial market stock data from images using the powerful Vision LLM Qwen2, all within a Gradio interface. This setup allows quick and easy extraction of key stock stats from screenshots and other image-based data sources—perfect for analysts, traders, and finance enthusiasts looking to streamline data processing. Watch to see how this AI tool can simplify your workflow and make stock data analysis faster and more efficient! 

 

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. 

 

FastAPI Endpoint for Sparrow LLM Agent

FastAPI Endpoint for Sparrow LLM Agent. I show how FastAPI endpoint is used in Sparrow to run LLM agent functionality from API client. 

 

Sparrow Parse API for PDF Invoice Data Extraction

I explain how Sparrow Parse API is integrated into Sparrow for data extraction from PDF documents, such as invoices, receipts, etc. 

 

Avoid LLM Hallucinations: Use Sparrow Parse for Tabular PDF Data, Instructor LLM for Forms

LLMs tend to hallucinate and produce incorrect results for table data extraction. For this reason in Sparrow we are using Instructor structured output for LLM to query form data and Sparrow Parse to process tabular data within the same document in combined approach. 

 

Instructor and Ollama for Invoice Data Extraction in Sparrow [LLM, JSON]

Structured output from invoice document, running local LLM. This works well with Instructor and Ollama.

 

Hybrid RAG with Sparrow Parse

To process complex layout docs and improve data retrieval from invoices or bank statements, we are implementing Sparrow Parse. It works in combination with LLM for form data processing. Table data is converted either into HTML or Markdown formats and extracted directly by Sparrow Parse. I explain Hybrid RAG idea in this video. 

 

Sparrow Parse - Data Processing for LLM

Data processing in LLM RAG is very important, it helps to improve data extraction results, especially for complex layout documents, with large tables. This is why I build open source Sparrow Parse library, it helps to balance between LLM and standard Python data extraction methods. 

 

Invoice Data Preprocessing for LLM

Data preprocessing is important step for LLM pipeline. I show various approaches to preprocess invoice data, before feeding it to LLM. This is quite challenging step, especially to preprocess tables. 

 

You Don't Need RAG to Extract Invoice Data

Documents like invoices or receipts can be processed by LLM directly, without RAG. I explain how you can do this locally with Ollama and Instructor. Thanks to Instructor, structured output from LLM can be validated with your own Pydantic class. 

 

LLM JSON Output with Instructor RAG and WizardLM-2

With Instructor library you can implement simple RAG without Vector DB or dependencies to other LLM libraries. The key RAG components - good data pre-processing and cleaning, powerful local LLM (such as WizardLM-2, Nous Hermes 2 PRO or Llama3) and Ollama or MLX backend.

Local LLM RAG with Unstructured and LangChain [Structured JSON]

Using unstructured library to pre-process PDF document content, to be in a cleaner format. This helps LLM to produce more accurate response. JSON response is generated thanks to Nous Hermes 2 PRO LLM. Without any additional post-processing. Using Pydantic dynamic class to validate response to make sure it matches request. 

 

LlamaIndex Upgrade to 0.10.x Experience

I explain key points you should keep in mind when upgrading to LlamaIndex 0.10.x. 

 

LLM Structured Output for Function Calling with Ollama

I explain how function calling works with LLM. This is often confused concept, LLM doesn't call a function - LLM retuns JSON response with values to be used for function call from your environment. In this example I'm using Sparrow agent, to call a function. 

 

Optimizing Receipt Processing with LlamaIndex and PaddleOCR

LlamaIndex Text Completion function allows to execute LLM request combining custom data and the question, without using Vector DB. This is very useful when processing output from OCR, it simplifies the RAG pipeline. In this video I explain, how OCR can be combined with LLM to process image documents in Sparrow.

 

LlamaIndex Multimodal with Ollama [Local LLM]

I describe how to run LlamaIndex Multimodal with local LlaVA LLM through Ollama. Advantage of this approach - you can process image documents with LLM directly, without running through OCR, this should lead to better results. This functionality is integrated as separate LLM agent into Sparrow.