# Illustrative: Dots-style transcript → timestamped RAG chunks

**Use case:** 

Illustrative sample only. Shows how SRT cues become timestamped JSON chunks. Not an official OpenAI transcript and not a verified public file we host.

## Input

```json
{
  "items": [
    {
      "rawText": "1\n00:00:01,000 --> 00:00:06,000\nToday we are introducing Dots, always-on AI agents powered by GPT-6 Astra.\n\n2\n00:00:06,000 --> 00:00:12,000\nEach Dot gets its own cloud computer and connects to more than four thousand apps.\n\n3\n00:00:12,000 --> 00:00:18,000\nDots learn how you work and keep working in the background while you focus.\n\n4\n00:00:18,000 --> 00:00:24,000\nEnterprise admins can enable organization-wide Dots with policy controls.",
      "sourceName": "illustrative-dots-style-transcript",
      "chunkSize": 1500,
      "overlap": 150
    }
  ]
}
```

## Output

```json
{
  "sourceName": {
    "label": "sourceName",
    "format": "string"
  },
  "startTimestampMs": {
    "label": "startTimestampMs",
    "format": "integer"
  },
  "endTimestampMs": {
    "label": "endTimestampMs",
    "format": "integer"
  },
  "text": {
    "label": "text",
    "format": "string"
  },
  "chunkId": {
    "label": "chunkId",
    "format": "string"
  }
}
```

## How it works

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

The SRT & WebVTT to JSON Subtitle Chunker for RAG Actor is ready to run with the inputs already set for "Illustrative: Dots-style transcript → timestamped RAG chunks". [Sign up or log in](https://console.apify.com/create-task-from-example/r1W1jxSpbRssmEHoD) and you'll land in Apify Console with a copy of this exact task.

### 2. Start the "Illustrative: Dots-style transcript → timestamped RAG chunks" task

The SRT & WebVTT to JSON Subtitle Chunker for RAG Actor will start running based on the input automatically. You can also change all the input fields, like subtitle inputs, to match your specific use case.

### 3. Receive the "Illustrative: Dots-style transcript → timestamped RAG chunks" 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 sourcename, starttimestampms, endtimestampms, 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 "Illustrative: Dots-style transcript → timestamped RAG chunks" task via API](https://apify.com/skilled_glee/subtitle-rag-chunker.md).

## About this Actor

This example demonstrates how to use [SRT & WebVTT to JSON Subtitle Chunker for RAG](https://apify.com/skilled_glee/subtitle-rag-chunker.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/skilled_glee/subtitle-rag-chunker.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/skilled_glee/subtitle-rag-chunker.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`).