LLM JSON Output with Instructor RAG and WizardLM-2

With Instructor library you can implement simple RAG without Vector DB or dependencies to other LLM libraries. The key RAG components - good data pre-processing and cleaning, powerful local LLM (such as WizardLM-2, Nous Hermes 2 PRO or Llama3) and Ollama or MLX backend.

Local RAG Explained with Unstructured and LangChain

In this tutorial, I do a code walkthrough and demonstrate how to implement the RAG pipeline using Unstructured, LangChain, and Pydantic for processing invoice data and extracting structured JSON data.

 

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.