README.md

Generative AI notebooks

This folder contains notebooks that demonstrate various use cases for Elasticsearch as the retrieval engine and vector store for LLM-powered applications.

The following notebooks are available:

• Question answering
• Chatbot

Notebooks

Question answering

In the question-answering.ipynb notebook you'll learn how to:

• Retrieve sample workplace documents from a given URL.
• Set up an Elasticsearch client.
• Chunk documents into 512 token passages with an overlap of 256 token using the RecursiveCharacterTextSplitter from langchain.
• Use OpenAIEmbeddings from langchain to create embeddings for the content.
• Retrieve embeddings for the chunked passages using OpenAI.
• Persist the passage documents along with their embeddings into Elasticsearch.
• Set up a question-answering system using OpenAI and ElasticKnnSearch from langchain to retrieve answers along with their source documents.

Chatbot

In the chatbot.ipynb notebook you'll learn how to:

• Retrieve sample workplace documents from a given URL.
• Set up an Elasticsearch client.
• Chunk documents into 512 token passages with an overlap of 256 token using the RecursiveCharacterTextSplitter from langchain.
• Use OpenAIEmbeddings from langchain to create embeddings for the content.
• Retrieve embeddings for the chunked passages using OpenAI.
• Run hybrid search in Elasticsearch to find documents that answers asked questions.
• Maintain conversational memory for follow-up questions.