Question Answering using Gemini, Langchain & Elasticsearch

This tutorial demonstrates how to use the Gemini API to create embeddings and store them in Elasticsearch. We will learn how to connect Gemini to private data stored in Elasticsearch and build question/answer capabilities over it using LangChian.

setup

• Elastic Credentials - Create an Elastic Cloud deployment to get all Elastic credentials (ELASTIC_CLOUD_ID, ELASTIC_API_KEY).

• GOOGLE_API_KEY - To use the Gemini API, you need to create an API key in Google AI Studio.

Install packages

Import packages and credentials

Get Credentials

Add documents

Let's download the sample dataset and deserialize the document.

Split Documents into Passages

Index Documents into Elasticsearch using Gemini Embeddings

Create a retriever using Elasticsearch

Format Docs

Create a Chain using Prompt Template + gemini-pro model