Andrej Baranovskij Blog
Blog about Oracle, Full Stack, Machine Learning and Cloud
Sunday, March 17, 2024
FastAPI File Upload and Temporary Directory for Stateless API
I explain how to handle file upload with FastAPI and how to process the file by using Python temporary directory. Files placed into temporary directory are automatically removed once request completes, this is very convenient for stateless API.
Sunday, March 10, 2024
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.
Labels:
LlamaIndex,
LLM,
RAG
Sunday, March 3, 2024
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.
Labels:
LlamaIndex,
LLM,
RAG
Monday, February 26, 2024
LLM Agents with Sparrow
I explain new functionality in Sparrow - LLM agents support. This means you can implement independently running agents, and invoke them from CLI or API. This makes it easier to run various LLM related processing within Sparrow.
Tuesday, February 20, 2024
Extracting Invoice Structured Output with Haystack and Ollama Local LLM
I implemented Sparrow agent with Haystack structured output functionality to extract invoice data. This runs locally through Ollama, using LLM to retrieve key/value pairs data.
Sunday, February 4, 2024
Local LLM RAG Pipelines with Sparrow Plugins [Python Interface]
There are many tools and frameworks around LLM, evolving and improving daily. I added plugin support in Sparrow to run different pipelines through the same Sparrow interface. Each pipeline can be implemented with different tech (LlamaIndex, Haystack, etc.) and run independently. The main advantage is that you can test various RAG functionalities from a single app with a unified API and choose the one that works best in the specific use case.
Monday, January 29, 2024
LLM Structured Output with Local Haystack RAG and Ollama
Haystack 2.0 provides functionality to process LLM output and ensure proper JSON structure, based on predefined Pydantic class. I show how you can run this on your local machine, with Ollama. This is possible thanks to OllamaGenerator class available from Haystack.
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