A simple interface for querying and managing your Pinecone vector database. Search through your documents using natural language queries.

To use PineconeIndex in your own Val Town project:

import PineconeIndex from "https://esm.town/v/peterqliu/PineconeIndex/PineconeIndex";

const pineconeKey = Deno.env.get("PINECONE_KEY");
const modelToken = Deno.env.get("OPENAI_KEY");

const index = new PineconeIndex({
  name: "2025-all-docs",
  model: "text-embedding-ada-002",
  dimensions: 1536,
  pineconeKey,
  modelToken,
});

// Use the methods directly
const results = await index.query("machine learning applications");
await index.upsertRecords(["Document 1", "Document 2"]);
await index.empty();

Find the most relevant documents for your query.

const results = await index.query("your search text");

Returns:

{
  "matches": [
    {
      "id": "doc-123",
      "score": 0.95,
      "metadata": {
        "text": "Machine learning is transforming..."
      }
    }
  ]
}

Upload new documents to your index.

await index.upsertRecords([
  "First document content",
  "Second document content"
]);

Remove all documents from your index.

await index.empty();

Create a new Pinecone index (if it doesn't exist).

await index.create();

import PineconeIndex from "https://esm.town/v/peterqliu/PineconeIndex/PineconeIndex";

const index = new PineconeIndex({
  name: "my-documents",
  model: "text-embedding-ada-002", 
  dimensions: 1536,
  pineconeKey: Deno.env.get("PINECONE_KEY"),
  modelToken: Deno.env.get("OPENAI_KEY"),
});

// Add some documents
await index.upsertRecords([
  "AI is revolutionizing healthcare",
  "Machine learning enables automation",
  "Solar energy is becoming more efficient"
]);

// Search for relevant documents
const results = await index.query("renewable energy technologies");
console.log(results.matches);

Create an HTTP endpoint that uses your index:

// query index with string for nearest neighbors
export default async function (req: Request): Promise<Response> {
  return await index.handleRequest(req);
}