Mistral Chat Completions with Elasticsearch Inference API

This notebook demonstrates how to set up a Mistral chat completion inference endpoint in Elasticsearch and stream chat responses using the inference API

Prerequisites

• Elasticsearch cluster
• Elasticsearch API key
• Mistral API key

Configuration

Set up your Elasticsearch and Mistral API credentials. For security, consider using environment variables.

Create the Inference Endpoint

Create the Mistral chat completion endpoint if it doesn't exist.

Chat Streaming Functions

Let's create functions to handle streaming chat responses from the inference endpoint.

Testing the Inference Endpoint

Now let's test our inference endpoint with a simple question. This will demonstrate streaming responses are working well from Elasticsearch.

Context Engineering with Elasticsearch

In this section, we'll demonstrate how to:

1. Index documents into Elasticsearch
2. Search for relevant context
3. Use retrieved documents to enhance our chat completions with contextual information

This approach combines retrieval-augmented generation (RAG) with Mistral's chat capabilities through Elasticsearch.

Step 1: Index some documents

First, let's create an Elasticsearch index to store our documents with both text content and vector embeddings for semantic search.

Step 2: Search for Relevant Context

Now let's create a function to search our indexed documents for relevant context based on a user's query.

Step 3: RAG-Enhanced Chat Function

Now let's create a function that combines document retrieval with our Mistral chat completion for contextual responses.