# Is the project dead?
The project [commit history](https://github.com/RasaHQ/rasa/commits/main/) shows that status of OS. You should try the [developer edition](/content/rasa-pro-developer-edition-license-key-request/index.html) and Rasa CALM.

# Getting Error: Type Error: 'NoneType' object is not iterable
It looks like your local instance is running Rasa Pro but your docker-compose is using a Rasa OSS image. Change the [image](https://hub.docker.com/r/rasa/rasa-pro/tags) in your docker compose from rasa/rasa to rasa/rasa-pro and don’t forget to set the RASA_PRO_LICENSE env var.

# Programmatic Interaction with Chatbot in RASA
Yes, you can use the [REST](/content/docs/reference/channels/your-own-website#rest-channels/index.html) channel. Also discussed [here](/content/docs/openapi/http-api/#tag/Domain/operation/getDomain/index.html).

# Frontend Widget Not Receiving Bot Responses
Add --debug and see if confirm that the Rasa sees the message from the user.

# Can we use rasa command generation model and other model at the same time?
You can read about configuring models in the docs [here](/content/docs/rasa-pro/concepts/components/llm-configuration-from-3-11/index.html)

# Encountered empty domain during validation
Add --domain domain.yml to the command line.

# Problem installing rasa x on ubuntu
It was [announced](https://forum.rasa.com/t/rasa-x-community-edition-is-no-longer-maintained-or-supported/53656) in June 2022 that Rasa X is no longer maintained or supported.

# Rasa X (Community Edition) is no longer maintained or supported

# Rasa-Pro setup AVX Issue on macOS M1
Sounds like the wrong tensorflow. My install steps on an M1:

```bash
uv venv
source .venv/bin/activate
uv pip install rasa-pro==3.10.10
uv pip list | grep tensorflow
```

# Rasa entities extraction which appears to be similar for slot filling via forms
Are you using a form? Can you give some example utterances that cause issues?

# Can Rasa CALM handle image generation if integrated with the right LLM
You could do this via a custom action.

# Recursion in Rasa Flows, Compatible LLM Models, and Fine-Tuning Datasets - Rasa Pro CALM
You can use CRV’s with other slot types too. Here’s a [blog post](/content/blog/conditional-response-variations/index.html) showing a categorical.

# How to add predefined Chitchat and FAQs to CALM
There’s a built in chitchat feature by default. It’s called [pattern_completed](/content/docs/rasa-pro/concepts/conversation-repair/#default-behavior/index.html). If you don’t want to use the built-in RAG feature then you could create flows for each FAQ or you could use the NLU based [response selector](/content/docs/rasa-pro/nlu-based-assistants/chitchat-faqs/index.html).

# Issue with Ollama LLM Integration - Port Binding and Quota Exceeded - RASA CALM
It could be the response rephraser which uses openai got-3.5 by default. It’s documented [here](/content/docs/rasa-pro/concepts/contextual-response-rephraser/#llm-configuration/index.html) and I would disable it for now in your endpoints.yml. If that’s not the issue, post the full logfile someplace and I will review.

# Integrating Rasa with a Custom Frontend Framework
You can read about using the Socket or REST channels with your widget [here](/content/docs/rasa/connectors/your-own-website/index.html).

# Sending additional data to RASA server in REST request
You can supply metadata on the rest call and then retrieve that in action_session_start. There’s an example of this [here](/content/docs/rasa-pro/concepts/default-actions/#customization/index.html). To supply the metadata via REST, here’s an example payload:

```json
{
  "sender": "test_user", // sender ID of the user sending the message
  "message": "Hi there!"
  "metadata": {
    ...
  }
}
```

# RASA Integration with server
You should review the [training materials](https://learning.rasa.com/) which do a good job covering the basics. To access services from the bot, this is done with custom actions.

# Safe to delete contents of .rasa/cache/…?
Yes, it’s safe to delete the cache. It will mean that all components will have to re-train even if no changes were made.