I built a custom connector (by adding a channel.py file) and tested it locally (by interacting with it using postman) via the custom channel. While deploying the bot, I used the following command to set the environment variable to connect with the custom channel: export ADDITIONAL_CHANNEL_CREDENTIALS="rest='channel.RestInput1'" I am able to interact with the deployed bot using by sending post requests (using postman) via rest channel. But, on trying to interact via custom channel, I get the following response: <!DOCTYPE html> <meta charset=UTF-8> <title>404 — Not Found</title> <style> html { font-family: sans-serif } </style> <h1>⚠️ 404 — Not Found</h1> <p>Requested URL /webhooks/custom_channel/webhook not found My channel.py file is stored in the repository connected to Rasa X. Is there an additional step I am missing?

Hi @gaushh , When deploying on kubernetes, this is how I always do it for deploying custom channels & components. I create a custom docker image of rasa that includes my custom channel as an installed python package, using these steps: Create this folder structure with files whose content is described below: |-custom_channels | |channel.py | |__init__.py | |setup.py |Dockerfile The folder custom_channels is just your code, with folder structure of a python package. The file setup.py , is a minimalistic setup file to install the package. Content of setup.py is something like this: """A setuptools based setup module. See: https://packaging.python.org/guides/distributing-packages-using-setuptools/ https://github.com/pypa/sampleproject """ from setuptools import setup, find_packages setup( name='custom-channels', version='0.0.1', url='', author='', author_email='', description='Custom Channels for Rasa', packages=find_packages(), install_requires=[], ) During development of your channel, it is useful to just install it inside your python environment, like a conda environment, in editable mode, using this command: pip install -e . And then in your bot, use a credentials.yml as defined below. Once ready to deploy, the file Dockerfile is used to build the custom docker image of Rasa with your custom channel included: # # Notes: # (-) See https://github.com/RasaHQ/rasa/blob/master/docker/Dockerfile_full # FROM rasa/rasa:1.10.16-full ################################################## # Install the custom channel as a python package # ################################################## USER root # change working directory WORKDIR /build-custom # copy files COPY custom_channels /build-custom/custom_channels COPY setup.py /build-custom # install custom channels RUN pip install --upgrade pip RUN python -m pip install . # cleanup RUN rm -rf /build-custom ################################## # change user to not run as root # ################################## WORKDIR /app USER 1001 Once you have this in place, you Build the docker image with: sudo docker build . --no-cache --file Dockerfile -t custom-rasa:0.0.1 You then push this custom rasa image to your docker registry, and use it to deploy in your k3s cluster. Note that an alternative to k3s is microk8s, which has a build in container registry. If you use microk8s, your workflow will become a bit easier, because you can just build the docker image on your computer or VM and push it directly into the cluster, without going through an external docker registry. To deploy this custom rasa container in the cluster, I prefer to deploy using a helm-chart, as described here . For the Rasa Open Source Version , you add this in the values.yml: # rasa: Settings common for all Rasa containers rasa: # https://rasa.com/docs/rasa/user-guide/connectors/custom-connectors/#id1 # name of the Custom Rasa image to use # Prefix it with the docker-registry you us…

Interact with the deployed bot (on k3s using quick install script) using custom connectors

Rasa Open Source

gaushh (Gaurav Sharma) November 19, 2020, 11:10am 5

@arjaan I deployed the pod following your guidelines. Now I pod doesn’t reem to be running. I can see the following error but am unable to resolve it. Kindly take a look into it.

Rasa 2.0 Custom Connector deployment on Rasax (33.x) docker

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Interact with the deployed bot (on k3s using quick install script) using custom connectors

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