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add github actions for test and build
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.github/workflows/api-build.yml

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name: API Build
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on:
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push:
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branches: [main]
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paths:
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- 'services/query-engine/**'
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jobs:
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build:
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name: Build
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- name: Docker build
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run: docker build -t query-engine-api:${{ github.sha }} services/query-engine

.github/workflows/api-tests.yml

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name: API Tests
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on:
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push:
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branches: [main]
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paths:
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- 'services/query-engine/**'
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jobs:
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test:
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name: Test
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python 3.13
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uses: actions/setup-python@v5
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with:
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python-version: '3.13'
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- name: Install Poetry
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run: pip install poetry==2.3.2
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- name: Install dependencies
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working-directory: services/query-engine
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run: poetry install --no-root --no-interaction
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- name: Run tests
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working-directory: services/query-engine
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run: poetry run pytest tests/ -v

.github/workflows/ui-build.yml

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name: UI Build
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on:
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push:
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branches: [main]
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paths:
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- 'ui/**'
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jobs:
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build:
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name: Build
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- name: Docker build
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run: docker build -t query-engine-ui:${{ github.sha }} ui

README.md

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# Agent Query Engine
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[![API Tests](https://github.com/Yukigeshiki/agent-query-engine-python/actions/workflows/api-tests.yml/badge.svg)](https://github.com/Yukigeshiki/agent-query-engine-python/actions/workflows/api-tests.yml)
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[![API Build](https://github.com/Yukigeshiki/agent-query-engine-python/actions/workflows/api-build.yml/badge.svg)](https://github.com/Yukigeshiki/agent-query-engine-python/actions/workflows/api-build.yml)
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[![UI Build](https://github.com/Yukigeshiki/agent-query-engine-python/actions/workflows/ui-build.yml/badge.svg)](https://github.com/Yukigeshiki/agent-query-engine-python/actions/workflows/ui-build.yml)
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A document ingestion and query engine built with Python and FastAPI. Upload documents (PDF, DOCX, TXT), extract knowledge graph triplets and vector embeddings, then query across both using natural language. Documents are stored in GCS, processed asynchronously via Celery, and indexed into Neo4j (knowledge graph) and pgvector (vector embeddings). The UI provides document upload with interactive graph visualization and a query interface with retrieval mode selection.
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## How It Works

services/query-engine/app/api/v1/health.py

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async def health_check(
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kg_service: KnowledgeGraphService | None = Depends(get_optional_kg_service),
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) -> JSONResponse:
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"""Return application health status, version, and component health."""
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"""Return application health status, version, and component health. """
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components: dict[str, ComponentHealth] = {}
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if kg_service is not None:

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