Retrieve Results

After storing embeddings in a vector database, you need to query them to find similar content. The query method returns the most semantically similar chunks to your input embedding, ranked by relevance. This example shows how to retrieve similar chunks from a Pinecone vector database.

import { MDocument, embed, PineconeVector } from "@mastra/rag";
 
const doc = MDocument.fromText("Your text content...");
 
const chunks = await doc.chunk();
 
const { embeddings } = await embed(chunks, {
  provider: "OPEN_AI",
  model: "text-embedding-ada-002",
  maxRetries: 3,
});
 
const pinecone = new PineconeVector("your-api-key");
 
await pinecone.createIndex("test_index", 1536);
 
await pinecone.upsert(
  "test_index",
  embeddings,
  chunks?.map((chunk: any) => ({ text: chunk.text })),
);
 
const results = await pinecone.query("test_index", embeddings[0], 10);
 
console.log(results);

View Example on GitHub

Insert Embedding in PgVector Basic RAG