Ship the Phase 4 accuracy eval suite with a manual Gitea workflow, emit versioned WS status frames during grounded chat, and introduce opt-in agent infrastructure (Redis/Celery, AgentRun/Step, tools, orchestrator) gated by ALLOW_AGENTIC_TASKS so default chat behaviour stays unchanged.
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name: Run Evals
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# Manual eval harness — does not gate PRs (#62 Phase 4).
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# Requires self-hosted runner with GPU/Ollama and live search when SKIP_LIVE is unset.
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on:
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workflow_dispatch: {}
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jobs:
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run-evals:
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runs-on: self-hosted
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steps:
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- name: Checkout
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uses: actions/checkout@v4
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- name: Install uv
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run: |
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curl -LsSf https://astral.sh/uv/install.sh | sh
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echo "$HOME/.local/bin" >> "$GITHUB_PATH"
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- name: Install dependencies
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run: uv sync --frozen
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- name: Validate eval suite (offline)
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env:
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DJANGO_ENV: dev
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DJANGO_SECRET_KEY: test-secret-key
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DJANGO_DEBUG: "true"
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DJANGO_ALLOWED_HOSTS: localhost,127.0.0.1,testserver
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DATABASE_URL: ""
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DB_HOST: ""
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SKIP_RAG_INIT: "1"
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SKIP_LIVE: "1"
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OLLAMA_BASE_URL: http://127.0.0.1:11434
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working-directory: llm_be
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run: uv run python manage.py run_evals --dry-run
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- name: Run eval harness
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env:
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DJANGO_ENV: dev
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DJANGO_SECRET_KEY: test-secret-key
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DJANGO_DEBUG: "true"
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DJANGO_ALLOWED_HOSTS: localhost,127.0.0.1,testserver
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DATABASE_URL: ""
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DB_HOST: ""
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SKIP_RAG_INIT: "1"
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RUN_EVALS: "1"
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ALLOW_INTERNET_ACCESS: "true"
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OLLAMA_BASE_URL: http://127.0.0.1:11434
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working-directory: llm_be
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run: |
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uv run python manage.py run_evals \
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--runs 3 \
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--output "../eval-report.json"
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- name: Upload eval report
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if: always()
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uses: actions/upload-artifact@v4
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with:
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name: eval-report
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path: eval-report.json
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