Mistral AI

Mistral AI is a language model provider. Check out Mistral AI Technology for more information about their models and pricing.

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Install RAG Chat SDK

Initialize the project and install the required packages:

npm init es6
npm install dotenv
npm install @upstash/rag-chat

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Setup Upstash Redis

Create a Redis database using Upstash Console or Upstash CLI and copy the UPSTASH_REDIS_REST_URL and UPSTASH_REDIS_REST_TOKEN into your .env file.

UPSTASH_REDIS_REST_URL=<YOUR_URL>
UPSTASH_REDIS_REST_TOKEN=<YOUR_TOKEN>

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Setup Upstash Vector

Create a Vector index using Upstash Console or Upstash CLI and copy the UPSTASH_VECTOR_REST_URL and UPSTASH_VECTOR_REST_TOKEN into your .env file.

UPSTASH_VECTOR_REST_URL=<YOUR_URL>
UPSTASH_VECTOR_REST_TOKEN=<YOUR_TOKEN>

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Setup Mistral AI

Create a Mistral AI account and get an API key from Mistral AI Console -> La Plateforme -> API Keys. Set your Mistral AI API key as an environment variable:

MISTRAL_AI_KEY=<YOUR_API_KEY>

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Setup the Project

Initialize RAGChat with the Mistral AI model:

import { RAGChat, mistralai } from "@upstash/rag-chat";
import "dotenv/config";

export const ragChat = new RAGChat({
  model: mistralai("mistral-small-latest",{apiKey: process.env.MISTRAL_AI_KEY}),
});

Add context to the RAG Chat:

await ragChat.context.add("The speed of light is approximately 299,792,458 meters per second.");

Chat with the RAG Chat:

const response = await ragChat.chat("What is the speed of light?");
console.log(response);

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Run

Run the project:

npx tsx index.ts