Vectorization failed 404 http://host.docker.internal:11434/api/embed

hi @Kieran_Sears !!

Welcome to our community

I was just playing around with Verba + Ollama all in docker

I am not sure exactly how WSL2 plays with windows + docker, but can you try running everything in docker?

One thing to note: Whenever you start Verba, your ollama must have the models available, otherwise they will not be listed in Verba. Verba will connect to Ollama at startup and read all available models.

Here is how I am doing:

first, create a docker-compose.yaml file like this:

---

services:
  verba:
    image: semitechnologies/verba
    ports:
      - 8000:8000
    environment:
      - WEAVIATE_URL_VERBA=http://weaviate:8080
      - OLLAMA_URL=http://ollama:11434
      - OLLAMA_MODEL=llama3.2
      - OLLAMA_EMBED_MODEL=llama3.2

    volumes:
      - ./data:/data/
    depends_on:
      weaviate:
        condition: service_healthy
    healthcheck:
      test: wget --no-verbose --tries=3 --spider http://localhost:8000 || exit 1
      interval: 5s
      timeout: 10s
      retries: 5
      start_period: 10s

  weaviate:
    command:
      - --host
      - 0.0.0.0
      - --port
      - '8080'
      - --scheme
      - http
    image: semitechnologies/weaviate:1.25.10
    ports:
      - 8080:8080
      - 3000:8080
    volumes:
      - weaviate_data:/var/lib/weaviate
    restart: on-failure:0
    healthcheck:
      test: wget --no-verbose --tries=3 --spider http://localhost:8080/v1/.well-known/ready || exit 1
      interval: 5s
      timeout: 10s
      retries: 5
      start_period: 10s
    environment:
      OPENAI_APIKEY: $OPENAI_API_KEY
      COHERE_APIKEY: $COHERE_API_KEY
      QUERY_DEFAULTS_LIMIT: 25
      AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true'
      PERSISTENCE_DATA_PATH: '/var/lib/weaviate'
      ENABLE_MODULES: 'e'
      CLUSTER_HOSTNAME: 'node1'

  ollama:
    image: ollama/ollama:0.3.14
    volumes:
      - ollama_data:/root/.ollama
    ports:
      - 11434:11434
      
volumes:
  weaviate_data: {}
  ollama_data: {}
...

Now, let’s make sure we have the model we selected (in this case, llama3.2) available:

docker compose exec -ti ollama ollama pull llama3.2

You can check if the model is listed here:
http://localhost:11434/api/tags

ok, now we can start everything up:

docker compose up -d

Now proceed to import a document at verba, that should be running at:
http://localhost:8000/

A little after the import start, you should see ollama eating up resources:

Obs: llama was quite slow to vectorize and while doing with large documents, it was crashing

Let me know if this helps!

THanks!