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. 

 

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.