Artificial Intelligence

📚 Documentation

I'm following the "Lightning in 15 minutes" tutorial, and in the code snippet of the second section, there is a reference to a MNIST dataset from the tests/ directory.

![image](https://user-images.githubusercontent.com/30731072/169685554-7da2d549-09a5-4506-b99e-c6804d

URLS with the issue:

• https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.log_param
• https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.log_metric
• https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.set_tag

Description of proposal:

Document the maximum value and legal characters for log_param, log_metric and set_tag. Note that log_metric's value i

Is there an existing issue for this?

• I have searched the existing issues

What would you like to be added?

Current chunk manager didn't accept ctx for timeout and tracing control.
such as:
Read(ctx, Context, filePath string) ([]byte, error)

Why is this needed?

No response

Anything else?

No response

We support toml as params file. There are a few issues with our current toml parsing:

1. We use toml library, which is not toml 1.0 standard compatible. Also, the library is not being actively maintained.
2. toml dumping does not preserve existing formatting.
3. iterative/dvc#6402

We can try migrating to tomli (which

Specs

• Leon version: latest
• OS (or browser) version: Fedora 30
• Node.js version: 10.16.3
• Complete "npm run check" output:

➡ Here is the diagnosis about your current setup
✔ Run
✔ Run modules
✔ Reply you by texting
❗ Amazon Polly text-to-speech
❗ Google Cloud text-to-speech
❗ Watson text-to-speech
❗ Offline text-to-speech
❗ Google Cloud speech-to-text
❗ Watson spee

Yes

TikaConverter requires tika to be running, which can be achieved by executing docker run -p 9998:9998 apache/tika:1.24.1. However, to simplify the usage of TikaConverter for users, it would be nice to have a method to launch the docker container from within Haystack. As an example, for our document stores, we have launch methods, such as [launch_es()](https://github.com/deepset-ai/haystack