docs: add Vercel AI SDK

add Vercel AI SDK to basic RAG guide and to clients page

docs/getting-started/clients.mdx

@@ -87,6 +87,9 @@ We recommend reaching out to the respective maintainers for any assistance or in
 ### Java
 [langchain4j](https://github.com/langchain4j/langchain4j)
 
+### JavaScript / TypeScript
+[Vercel AI SDK](https://github.com/vercel/ai)
+
 ### PHP
 [HelgeSverre/mistral](https://github.com/HelgeSverre/mistral)
 [partITech/php-mistral](https://github.com/partITech/php-mistral)

docs/guides/basic-RAG.md

@@ -12,6 +12,7 @@ Retrieval-augmented generation (RAG) is an AI framework that synergizes the capa
 - RAG with Mistral and LangChain
 - RAG with Mistral and LlamaIndex
 - RAG with Mistral and Haystack
+- RAG with Mistral and Vercel AI SDK
 
 <a target="_blank" href="https://colab.research.google.com/github/mistralai/cookbook/blob/main/mistral/rag/basic_RAG.ipynb">
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
@@ -322,3 +323,70 @@ print(result["llm"]["replies"][0].content)
 ```
 The two main things the author worked on before college were writing and programming. He wrote short stories, which he admitted were awful, and essays about various topics. He also worked on spam filters and painted. Additionally, he started having dinners for a group of friends every Thursday night, which taught him how to cook for groups. He also bought a building in Cambridge to use as an office. The author was drawn to writing essays, which he started publishing online, and this helped him figure out what to work on. He also experimented with painting and studied AI in college.
 ```
+
+## RAG with Vercel AI SDK
+
+**Code:**
+```typescript
+import fs from "fs";
+import path from "path";
+import dotenv from "dotenv";
+import { mistral } from "@ai-sdk/mistral";
+import { cosineSimilarity, embed, embedMany, generateText } from "ai";
+
+dotenv.config();
+
+async function main() {
+  const db: { embedding: number[]; value: string }[] = [];
+
+  const essay = fs.readFileSync(path.join(__dirname, "essay.txt"), "utf8");
+  const chunks = essay
+    .split(".")
+    .map((chunk) => chunk.trim())
+    .filter((chunk) => chunk.length > 0 && chunk !== "\n");
+
+  const { embeddings } = await embedMany({
+    model: mistral.embedding("mistral-embed"),
+    values: chunks,
+  });
+  embeddings.forEach((e, i) => {
+    db.push({
+      embedding: e,
+      value: chunks[i],
+    });
+  });
+
+  const input =
+    "What were the two main things the author worked on before college?";
+
+  const { embedding } = await embed({
+    model: mistral.embedding("mistral-embed"),
+    value: input,
+  });
+  const context = db
+    .map((item) => ({
+      document: item,
+      similarity: cosineSimilarity(embedding, item.embedding),
+    }))
+    .sort((a, b) => b.similarity - a.similarity)
+    .slice(0, 3)
+    .map((r) => r.document.value)
+    .join("\n");
+
+  const { text } = await generateText({
+    model: mistral("open-mixtral-8x7b"),
+    prompt: `Answer the following question based only on the provided context:
+             ${context}
+
+             Question: ${input}`,
+  });
+  console.log(text);
+}
+
+main().catch(console.error);
+```
+
+**Output:**
+```
+The two main things the author worked on before college were writing and programming.
+```