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62 lines
2.2 KiB
YAML
62 lines
2.2 KiB
YAML
name: test | ollama
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on:
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workflow_call:
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env:
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COGNEE_SKIP_CONNECTION_TEST: 'true'
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jobs:
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run_llama-cpp_test:
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# needs ~4 Gb RAM for the GGUF model in a container which the smallest runner has
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runs-on: ubuntu-22.04
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steps:
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- name: Checkout repository
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uses: actions/checkout@v6
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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: '3.13.x'
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extra-dependencies: postgres llama-cpp
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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: Download Phi-3.5 GGUF model from S3
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# Mirrored from huggingface.co/bartowski/Phi-3.5-mini-instruct-GGUF (MIT)
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# into our bucket to avoid HuggingFace 429 rate limits in CI.
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# Phi-3.5-mini reliably emits the required per-node `description`;
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# the previous Phi-3-mini-q4 dropped it, failing extraction.
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env:
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AWS_ACCESS_KEY_ID: ${{ secrets.AWS_S3_DEV_USER_KEY_ID }}
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AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_S3_DEV_USER_SECRET_KEY }}
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AWS_DEFAULT_REGION: eu-west-1
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BUCKET: github-runner-cognee-tests
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MODEL_KEY: nightly_ci_artifacts/huggingface_models/Phi-3.5-mini-instruct-Q4_K_M.gguf
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MODEL_SHA256: e4165e3a71af97f1b4820da61079826d8752a2088e313af0c7d346796c38eff5
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run: |
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set -euo pipefail
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aws s3 cp "s3://$BUCKET/$MODEL_KEY" ./Phi-3.5-mini-instruct-Q4_K_M.gguf
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echo "$MODEL_SHA256 ./Phi-3.5-mini-instruct-Q4_K_M.gguf" | sha256sum -c -
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- name: Run example test
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env:
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PYTHONFAULTHANDLER: 1
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LLM_PROVIDER: "llama_cpp"
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LLAMA_CPP_MODEL_PATH: "./Phi-3.5-mini-instruct-Q4_K_M.gguf"
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LLM_ENDPOINT: ""
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LLAMA_CPP_N_CTX: 4096
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EMBEDDING_PROVIDER: "openai"
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LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}
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LLM_ARGS: ${{ secrets.LLM_ARGS }}
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EMBEDDING_MODEL: "openai/text-embedding-3-large"
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EMBEDDING_DIMENSIONS: "3072"
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EMBEDDING_MAX_TOKENS: "8191"
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STRUCTURED_OUTPUT_FRAMEWORK: "instructor"
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LLM_INSTRUCTOR_MODE: ""
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run: uv run python ./examples/demos/simple_cognee_example.py
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