Issue with Ollama LLM Integration - Port Binding and Quota Exceeded - RASA CALM

Rasa CALM
11

Hi, Did-you manage to configure Rasa CALM with Ollama in way it is totally OPENAI independant? Even with Entreprise search, rephrase etc. fonctionnalities activated?

If I try “Hugginface” as embedding model, an API KEY is needed even if documentation mentionned this config as “in-memory” solution:

Environment variables: [‘HUGGINGFACE_API_KEY’] not set. Required for API calls

CALM also provides an option to load lightweight embedding models in-memory without needing them to be exposed over an API.

If I try one of the Ollama embedding model mentionned in Ollama web site, I got the following error:

ProviderClientAPIException:Failed to embed documents

RuntimeError: asyncio.run() cannot be called from a running event loop

Thanks for your help.

My current config.yml

recipe: default.v1
language: en
pipeline:
- name: CompactLLMCommandGenerator
  llm:
    model_group: ollama-gemma3-1b
  flow_retrieval:
    embeddings:
      model_group: text_embedding_model #huggingface_embedding_model
policies:
- name: RulePolicy    # Remplace FlowPolicy si problème
- name: MemoizationPolicy
assistant_id: 20250328-161232-caramelized-continent

and endpoints.yml

model_groups:
  - id: ollama-gemma3-1b 
    models:
      - provider: ollama
        api_base: "http://localhost:11434"
        model: gemma3:1b
  - id: text_embedding_model
    models:
      - provider: ollama
        api_base: "http://localhost:11434"
        model: mxbai-embed-large
  # - id: huggingface_embedding_model
  #   models:
  #     - provider: huggingface
  #       model: BAAI/bge-small-en-v1.5
  #       model_kwargs: # used during instantiation
  #         device: "cpu"
  #       encode_kwargs: # used during inference
  #         normalize_embeddings: true

I I run with