# Multiple annotators with different data

**URL:** https://support.prodi.gy/t/multiple-annotators-with-different-data/188
**Category:** Uncategorized
**Tags:** solved, api
**Created:** [January 4, 2018, 8:27pm UTC](https://support.prodi.gy/t/multiple-annotators-with-different-data/188 "2018-01-04T20:27:54Z")
**Posts on this page:** 1
**Showing post:** 2

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### Author: ![ines](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/ines/32/3_2.png) [@ines](https://support.prodi.gy/u/ines)
#### Post date: [January 4, 2018, 10:56pm UTC](https://support.prodi.gy/t/multiple-annotators-with-different-data/188/2 "2018-01-04T22:56:14Z")

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This definitely sounds feasible. Do you want each annotator to annotate with a model in the loop, or do you have static data you want to go through more or less in order?

> [@kevinrosenberg21](#):
>
> How can I attach something like a user\_id to the request sent to an API? (a valid user\_id and password combination will be required to access the prodigy server)

I think the best solution here would be to put your service in the middle and let it handle the authentication. So, the user "logs on" and makes a request to your service. Your service authenticates the user and creates a session token etc. If this was successful, your service will start a Prodigy session for the user, and pass the user ID and all other details to the recipe. The recipe will then communicate with your service and request a stream of tasks. Your service will know which user is making requests, so it can construct the stream accordingly.

You can also look at the `prodigy.serve` function in`prodigy/ __init__.py` if you want to implement your own solution that executes a recipe starts the Prodigy server. But I'm not even sure this will be necessary in your case.

> [@kevinrosenberg21](#):
>
> What JSON format does prodigy expect to receive or how can I set that up?

You can find more details on the exact formats in your `PRODIGY_README.html` (available for download with Prodigy). If you're looking for the format of the annotation tasks in the stream, see the "Annotation task formats" section. A stream is an iterable of dictionaries, with one dictionary describing an annotation task. So your API could simply return a list of objects, e.g. `[{"text": "hello world"}]` etc.

To avoid exhausting the stream, you might want to write a little wrapper that keeps making requests if the queue is running low. I posted a little example for a custom loader [in this thread](https://support.prodi.gy/t/best-practices-for-text-classifier-annotations/135/4) – the example was supposed to show how to implement your own data loader for Twitter etc., but you can also easily adapt it for your use case:

```python
def custom_loader():
    page = 0 # if API is paged, keep a counter
    while True:
        r = requests.get('http://some-api', params={'page': page})
        response = r.json()
        for item in response['results']: # or however it's structured
            yield {'text': item['text']} # etc.
        page += 1 # after page is exhausted, increment

```

You can also add any other custom properties to your annotation task – like a user identifier. Anything that you add to a task's `"meta"` object will be displayed in the bottom right corner of the annotation card in the web app.

When Prodigy processes a stream, it will assign an `_input_hash` based on the input text, and a `_task_hash` based on the input _and_ the features to annotate, e.g. the spans or labels. This lets you determine whether two tasks are the same. So your service can look at a the task hashes, and check if a user has already annotated a task. It can also check if the tasks that went out to the user all came back annotated – and if not (for example, if the user just closes their browser and doesn't save), send them out again.

If you haven't seen it already, there's also [this thread on using multiple annotator](https://support.prodi.gy/t/multiple-annotators-without-personal-repetition/49), in which I explain a bit more about the hashing.

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