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. 

 

Easy-to-Follow RAG Pipeline Tutorial: Invoice Processing with ChromaDB & LangChain

I explain the implementation of the pipeline to process invoice data from PDF documents. The data is loaded into Chroma DB's vector store. Through LangChain API, the data from the vector store is ready to be consumed by LLM as part of the RAG infrastructure. 

 

Vector Database Impact on RAG Efficiency: A Simple Overview

I explain the importance of Vector DB for RAG implementation. I show with a simple example, how data retrieval from Vector DB could affect LLM performance. Before data is sent to LLM, you should verify if quality data is fetched from Vector DB. 

 

JSON Output from Mistral 7B LLM [LangChain, Ctransformers]

I explain how to compose a prompt for Mistral 7B LLM model running with LangChain and Ctransformers to retrieve output as JSON string, without any additional text. 

 

Structured JSON Output from LLM RAG on Local CPU [Weaviate, Llama.cpp, Haystack]

I explain how to get structured JSON output from LLM RAG running using Haystack API on top of Llama.cpp. Vector embeddings are stored in Weaviate database, the same as in my previous video. When extracting data, a structured JSON response is preferred because we are not interested in additional descriptions.