/.github/workflows/test_ollama.yml

name: test | ollama

on:
  workflow_call:

env:
  COGNEE_SKIP_CONNECTION_TEST: 'true'

jobs:

  run_ollama_test:

    # TODO: needs 32 Gb RAM for phi4 in a container — GitHub-hosted larger runner
    runs-on: ubuntu-22.04
    steps:
      - name: Checkout repository
        uses: actions/checkout@v6

      - name: Cognee Setup
        uses: ./.github/actions/cognee_setup
        with:
          python-version: '3.11.x'

      - name: Install torch dependency
        run: |
          uv add torch

      - name: Start Ollama container
        run: |
          docker run -d --name ollama -p 11434:11434 ollama/ollama
          sleep 5
          docker exec -d ollama bash -c "ollama serve --openai"

      - name: Check Ollama logs
        run: docker logs ollama

      - name: Wait for Ollama to be ready
        run: |
          for i in {1..30}; do
            if curl -s http://localhost:11434/v1/models > /dev/null; then
              echo "Ollama is ready"
              exit 0
            fi
            echo "Waiting for Ollama... attempt $i"
            sleep 2
          done
          echo "Ollama failed to start"
          exit 1

      - name: Pull required Ollama models
        run: |
          curl -X POST http://localhost:11434/api/pull -d '{"name": "phi4"}'
          curl -X POST http://localhost:11434/api/pull -d '{"name": "qwen3-embedding:latest"}'

      - name: Call ollama API
        run: |
          curl -X POST http://localhost:11434/v1/chat/completions \
            -H "Content-Type: application/json" \
            -d '{
              "model": "phi4",
              "stream": false,
              "messages": [
                { "role": "system", "content": "You are a helpful assistant." },
                { "role": "user", "content": "Whatever I say, answer with Yes." }
              ]
            }'
          curl -X POST http://127.0.0.1:11434/api/embed \
            -H "Content-Type: application/json" \
             -d '{
              "model": "qwen3-embedding:latest",
            "input": "This is a test sentence to generate an embedding."
            }'

      - name: Dump Docker logs
        run: |
          docker ps
          docker logs $(docker ps --filter "ancestor=ollama/ollama" --format "{{.ID}}")

      - name: Download embedding tokenizer from S3
        # Mirrored from huggingface.co/Qwen/Qwen3-Embedding-8B (Apache-2.0) so the
        # embedding tokenizer loads offline and never hits HuggingFace 429s in CI.
        env:
          AWS_ACCESS_KEY_ID: ${{ secrets.AWS_S3_DEV_USER_KEY_ID }}
          AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_S3_DEV_USER_SECRET_KEY }}
          AWS_DEFAULT_REGION: eu-west-1
          BUCKET: github-runner-cognee-tests
          TOKENIZER_KEY: nightly_ci_artifacts/huggingface_models/qwen3-embedding-tokenizer.tar.gz
          TOKENIZER_SHA256: 8f5834d8791c8da03220feaccc2fe9c443e23b1092dcd99c576ef613990bbd00
        run: |
          set -euo pipefail
          aws s3 cp "s3://$BUCKET/$TOKENIZER_KEY" tokenizer.tar.gz
          echo "$TOKENIZER_SHA256  tokenizer.tar.gz" | sha256sum -c -
          tar -xzf tokenizer.tar.gz -C "$GITHUB_WORKSPACE"

      - name: Run example test
        env:
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
          PYTHONFAULTHANDLER: 1
          LLM_PROVIDER: "ollama"
          LLM_API_KEY: "ollama"
          LLM_ENDPOINT: "http://localhost:11434/v1/"
          LLM_MODEL: "phi4"
          EMBEDDING_PROVIDER: "ollama"
          EMBEDDING_MODEL: "qwen3-embedding:latest"
          EMBEDDING_ENDPOINT: "http://localhost:11434/api/embed"
          EMBEDDING_DIMENSIONS: "4096"
          # Load the tokenizer from the S3-mirrored local dir, fully offline.
          HUGGINGFACE_TOKENIZER: "${{ github.workspace }}/qwen3-embedding-tokenizer"
          HF_HUB_OFFLINE: "1"
          TRANSFORMERS_OFFLINE: "1"
        run: uv run python ./examples/demos/simple_cognee_example.py