Mistral

npm install @llamaindex/mistral

import { MistralAI } from "@llamaindex/mistral";

const llm = new MistralAI({
  model: "mistral-large-latest",
  apiKey: process.env.MISTRAL_API_KEY
});

const response = await llm.chat({
  messages: [
    { role: "user", content: "Explain AI safety" }
  ]
});

console.log(response.message.content);

const stream = await llm.chat({
  messages: [{ role: "user", content: "Write code for..." }],
  stream: true
});

for await (const chunk of stream) {
  process.stdout.write(chunk.delta);
}

import { tool } from "@llamaindex/core/tools";
import { z } from "zod";

const searchTool = tool({
  name: "search",
  description: "Search for information",
  parameters: z.object({
    query: z.string()
  }),
  execute: async ({ query }) => {
    return `Search results for: ${query}`;
  }
});

const response = await llm.chat({
  messages: [{ role: "user", content: "Search for AI news" }],
  tools: [searchTool]
});

const codeLLM = new MistralAI({
  model: "codestral-latest"
});

const response = await codeLLM.chat({
  messages: [
    {
      role: "user",
      content: "Write a function to calculate fibonacci numbers"
    }
  ]
});

console.log(response.message.content);

import { MistralAIEmbedding } from "@llamaindex/mistral";

const embedModel = new MistralAIEmbedding({
  model: "mistral-embed",
  apiKey: process.env.MISTRAL_API_KEY
});

const embedding = await embedModel.getTextEmbedding(
  "LlamaIndex is a data framework"
);

console.log(embedding.length); // 1024

MISTRAL_API_KEY=...

import { Settings } from "llamaindex";
import { MistralAI, MistralAIEmbedding } from "@llamaindex/mistral";

Settings.llm = new MistralAI({ model: "mistral-large-latest" });
Settings.embedModel = new MistralAIEmbedding();

import { Settings, VectorStoreIndex } from "llamaindex";
import { MistralAI } from "@llamaindex/mistral";

Settings.llm = new MistralAI({ model: "mistral-large-latest" });

const index = await VectorStoreIndex.fromDocuments(documents);
const queryEngine = index.asQueryEngine();

const response = await queryEngine.query({
  query: "Summarize the documents"
});