From Text to Vectors: Leveraging Weaviate for local RAG Implementation with LlamaIndex

Weaviate provides vector storage and plays an important part in RAG implementation. I'm using local embeddings from the Sentence Transformers library to create vectors for text-based PDF invoices and store them in Weaviate. I explain how integration is done with LlamaIndex to manage data ingest and LLM inference pipeline. 

 

Enhancing RAG: LlamaIndex and Ollama for On-Premise Data Extraction

LlamaIndex is an excellent choice for RAG implementation. It provides a perfect API to work with different data sources and extract data. LlamaIndex provides API for Ollama integration. This means we can easily use LlamaIndex with on-premise LLMs through Ollama. I explain a sample app where LlamaIndex works with Ollama to extract data from PDF invoices. 

 

Secure and Private: On-Premise Invoice Processing with LangChain and Ollama RAG

The Ollama desktop tool helps run LLMs locally on your machine. This tutorial explains how I implemented a pipeline with LangChain and Ollama for on-premise invoice processing. Running LLM on-premise provides many advantages in terms of security and privacy. Ollama works similarly to Docker; you can think of it as Docker for LLMs. You can pull and run multiple LLMs. This allows to switch between LLMs without changing RAG pipeline.