# Preview a real dataset: 30 EU tenders from TED

**Use case:** 

Previews a real public dataset, 30 German IT tenders from TED collected by our UK & EU Public Tenders actor: title and description are embedded, the notice ID keeps records stable across runs, and buyer, CPV code, dates and URL become filterable metadata.

## Input

```json
{
  "datasetId": "Gs4uipztv8m200gjr",
  "target": "pinecone",
  "createCollection": true,
  "qdrantDistance": "Cosine",
  "embeddingProvider": "openai",
  "textFields": [
    "title",
    "description"
  ],
  "idFields": [
    "id"
  ],
  "metadataFields": [
    "buyer",
    "buyerCountry",
    "mainCpvCode",
    "mainCpvDescription",
    "publishedDate",
    "deadline",
    "url"
  ],
  "storeText": true,
  "textMetadataKey": "text",
  "chunkSize": 1000,
  "chunkOverlap": 100,
  "skipUnchanged": true,
  "deleteStale": false,
  "syncKey": "apify",
  "dryRun": true,
  "offset": 0
}
```

## Output

```json
{
  "id": {
    "label": "Record ID",
    "format": "text"
  },
  "chunkIndex": {
    "label": "Chunk",
    "format": "number"
  },
  "chunkCount": {
    "label": "of",
    "format": "number"
  },
  "chars": {
    "label": "Characters",
    "format": "number"
  },
  "text": {
    "label": "Text to embed",
    "format": "text"
  },
  "metadata": {
    "label": "Metadata",
    "format": "object"
  }
}
```

## How it works

### 1. Sign up on Apify or log in

The Dataset to Pinecone & Qdrant Vector Export Actor is ready to run with the inputs already set for "Preview a real dataset: 30 EU tenders from TED". [Sign up or log in](https://console.apify.com/create-task-from-example/VRbqE9VIFr4GbzA9O) and you'll land in Apify Console with a copy of this exact task.

### 2. Start the "Preview a real dataset: 30 EU tenders from TED" task

The Dataset to Pinecone & Qdrant Vector Export Actor will start running based on the input automatically. You can also change all the input fields, like dataset, vector database, and more, to match your specific use case.

### 3. Receive the "Preview a real dataset: 30 EU tenders from TED" output

Monitor the progress in real time. You will be notified as soon as your dataset is complete and ready for review. Each result includes data such as record id, chunk, of, and more.

### 4. Integrate into your workflow

The final output is delivered in JSON, CSV, or Excel format, ready to be plugged into your workflow. You can also [run the "Preview a real dataset: 30 EU tenders from TED" task via API](https://apify.com/oldjard/dataset-to-pinecone-qdrant.md).

## About this Actor

This example demonstrates how to use [Dataset to Pinecone & Qdrant Vector Export](https://apify.com/oldjard/dataset-to-pinecone-qdrant.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/oldjard/dataset-to-pinecone-qdrant.md) to learn more, explore other use cases, and run it yourself.


## How to integrate an Actor?

This Task's input is already configured above. Use it as-is rather than inventing a new one.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/oldjard/dataset-to-pinecone-qdrant.md

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).