d4b0e2d6ca
<!-- .github/pull_request_template.md --> ## Description <!-- Please provide a clear, human-generated description of the changes in this PR. DO NOT use AI-generated descriptions. We want to understand your thought process and reasoning. --> ## Type of Change <!-- Please check the relevant option --> - [ ] Bug fix (non-breaking change that fixes an issue) - [ ] New feature (non-breaking change that adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to change) - [ ] Documentation update - [x] Code refactoring - [ ] Performance improvement - [ ] Other (please specify): ## Screenshots/Videos (if applicable) <!-- Add screenshots or videos to help explain your changes --> ## Pre-submission Checklist <!-- Please check all boxes that apply before submitting your PR --> - [ ] **I have tested my changes thoroughly before submitting this PR** - [ ] **This PR contains minimal changes necessary to address the issue/feature** - [ ] My code follows the project's coding standards and style guidelines - [ ] I have added tests that prove my fix is effective or that my feature works - [ ] I have added necessary documentation (if applicable) - [ ] All new and existing tests pass - [ ] I have searched existing PRs to ensure this change hasn't been submitted already - [ ] I have linked any relevant issues in the description - [ ] My commits have clear and descriptive messages ## DCO Affirmation I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin.
104 lines
3.1 KiB
YAML
104 lines
3.1 KiB
YAML
name: test | ollama
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on:
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workflow_call:
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jobs:
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run_ollama_test:
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# needs 16 Gb RAM for phi4
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runs-on: buildjet-4vcpu-ubuntu-2204
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# services:
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# ollama:
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# image: ollama/ollama
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# ports:
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# - 11434:11434
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steps:
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- name: Checkout repository
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uses: actions/checkout@v4
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- name: Cognee Setup
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uses: ./.github/actions/cognee_setup
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with:
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python-version: '3.11.x'
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- name: Install torch dependency
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run: |
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uv add torch
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# - name: Install ollama
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# run: curl -fsSL https://ollama.com/install.sh | sh
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# - name: Run ollama
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# run: |
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# ollama serve --openai &
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# ollama pull llama3.2 &
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# ollama pull avr/sfr-embedding-mistral:latest
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- name: Start Ollama container
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run: |
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docker run -d --name ollama -p 11434:11434 ollama/ollama
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sleep 5
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docker exec -d ollama bash -c "ollama serve --openai"
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- name: Check Ollama logs
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run: docker logs ollama
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- name: Wait for Ollama to be ready
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run: |
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for i in {1..30}; do
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if curl -s http://localhost:11434/v1/models > /dev/null; then
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echo "Ollama is ready"
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exit 0
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fi
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echo "Waiting for Ollama... attempt $i"
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sleep 2
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done
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echo "Ollama failed to start"
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exit 1
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- name: Pull required Ollama models
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run: |
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curl -X POST http://localhost:11434/api/pull -d '{"name": "phi4"}'
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curl -X POST http://localhost:11434/api/pull -d '{"name": "avr/sfr-embedding-mistral:latest"}'
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- name: Call ollama API
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run: |
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curl -X POST http://localhost:11434/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "phi4",
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"stream": false,
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"messages": [
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{ "role": "system", "content": "You are a helpful assistant." },
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{ "role": "user", "content": "Whatever I say, answer with Yes." }
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]
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}'
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curl -X POST http://127.0.0.1:11434/api/embed \
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-H "Content-Type: application/json" \
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-d '{
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"model": "avr/sfr-embedding-mistral:latest",
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"input": "This is a test sentence to generate an embedding."
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}'
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- name: Dump Docker logs
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run: |
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docker ps
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docker logs $(docker ps --filter "ancestor=ollama/ollama" --format "{{.ID}}")
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- name: Run example test
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env:
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OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
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PYTHONFAULTHANDLER: 1
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LLM_PROVIDER: "ollama"
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LLM_API_KEY: "ollama"
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LLM_ENDPOINT: "http://localhost:11434/v1/"
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LLM_MODEL: "phi4"
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EMBEDDING_PROVIDER: "ollama"
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EMBEDDING_MODEL: "avr/sfr-embedding-mistral:latest"
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EMBEDDING_ENDPOINT: "http://localhost:11434/api/embed"
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EMBEDDING_DIMENSIONS: "4096"
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HUGGINGFACE_TOKENIZER: "Salesforce/SFR-Embedding-Mistral"
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run: uv run python ./new-examples/demos/simple_default_cognee_pipelines_example.py
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