This repository contains a set of examples for demonstrating common AI use cases for applications built on top of MarkLogic. These examples are intended to serve as a starting point for your own applications; you are encouraged to copy and modify the code as needed.

The examples in this repository depend on the Azure OpenAI Service. They can be easily tailored to work with any LLM supported by the LLM framework used by each example. Note though that if you wish to execute these examples as-is, you will need an Azure OpenAI account and API key.

Setup

If you would like to try out the example programs, please follow these instructions.

RAG Examples

MarkLogic excels at supporting RAG, or “Retrieval-Augmented Generation”, via its schema-agnostic nature as well as it’s powerful and flexible indexing. This repository contains the following examples of RAG with MarkLogic:

• The LangChain project demonstrates RAG with Python, LangChain, and MarkLogic.
• The langchain4j project demonstrates RAG with Java, langchain4j, and MarkLogic.
• The LangChain.js project demonstrates RAG with JavaScript, LangChain.js, and MarkLogic.

Splitting / Chunking Examples

A RAG approach typically benefits from sending multiple smaller segments or “chunks” of text to an LLM. Please see this guide on splitting documents for more information on how to split your documents and why you may wish to do so.

Embedding examples

To utilize the vector queries shown in the RAG Examples listed above, embeddings - vector representations of text - should be added to your documents in MarkLogic. See this guide on adding embeddings for more information.