LlamaIndex Upgrade to 0.10.x Experience

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

 

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. 

 

JSON Output with Notus Local LLM [LlamaIndex, Ollama, Weaviate]

In this video, I show how to get JSON output from Notus LLM running locally with Ollama. JSON output is generated with LlamaIndex using the dynamic Pydantic class approach. 

 

FastAPI and LlamaIndex RAG: Creating Efficient APIs

FastAPI works great with LlamaIndex RAG. In this video, I show how to build a POST endpoint to execute inference requests for LlamaIndex. RAG implementation is done as part of Sparrow data extraction solution. I show how FastAPI can handle multiple concurrent requests to initiate RAG pipeline. I'm using Ollama to execute LLM calls as part of the pipeline. Ollama processes requests sequentially. It means Ollama will process API requests in the queue order. Hopefully, in the future, Ollama will support concurrent requests. 

 

Transforming Invoice Data into JSON: Local LLM with LlamaIndex & Pydantic

This is Sparrow, our open-source solution for document processing with local LLMs. I'm running local Starling LLM with Ollama. I explain how to get structured JSON output with LlamaIndex and dynamic Pydantic class. This helps to implement the use case of data extraction from invoice documents. The solution runs on the local machine, thanks to Ollama. I'm using a MacBook Air M1 with 8GB RAM.