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feat(alloydb): PoC create basic sample for 'Vector Search' #13453

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feat(alloydb): PoC create basic sample for 'Vector Search' #13453

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Description

  • Validate Kokoro CI test environment
  • Preparation for Vector Search Demo Web App on Flask. Based on Perform a vector search.

Checklist

@eapl-gemugami
feat(alloydb): PoC create basic sample for vector-search
40438ae 
- Validate Kokoro CI test environment
@product-auto-label product-auto-label bot added api: alloydb Issues related to the AlloyDB API. samples Issues that are directly related to samples. labels Jun 23, 2025
@eapl-gemugami eapl-gemugami added the snippet-bot:force-run Force snippet-bot runs its logic label Jun 23, 2025
@snippet-bot snippet-bot bot removed the snippet-bot:force-run Force snippet-bot runs its logic label Jun 23, 2025
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Summary of Changes

Hello @eapl-gemugami, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request establishes a foundational Proof of Concept for integrating AlloyDB's vector search capabilities within a Python application, specifically as a precursor to a Flask web application demo. It encompasses the necessary database schema definition, data population, and the core Python logic required to connect to AlloyDB, execute SQL commands, and perform vector similarity searches. The changes also include the addition of relevant dependencies and a robust test suite to ensure the functionality works as expected.

Highlights

  • Vector Search PoC: Introduced a foundational Proof of Concept (PoC) for performing vector searches using AlloyDB, laying the groundwork for a Flask web application demo.
  • Dependency Management: Added new Python dependencies in requirements.txt for Flask, AlloyDB connectivity, and requirements-test.txt for testing with pytest.
  • Database Schema & Data: Defined a sample database schema in example_data.sql including product and product_inventory tables, with the product table featuring a vector column and a scann index for efficient similarity search.
  • AlloyDB Integration Logic: Implemented core Python functions in vector_search.py for establishing secure connections to AlloyDB, executing arbitrary SQL queries, and specifically performing vector similarity searches.
  • Automated Testing: Included a comprehensive test suite in vector_search_test.py utilizing pytest to validate the end-to-end vector search functionality, including database setup and data insertion.
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Code Review

This pull request introduces a proof-of-concept for AlloyDB's Vector Search feature. I've identified a potential resource leak in the database handling logic and a bug in the test data. Additionally, I've provided suggestions to improve type hinting and file paths in tests. Addressing these points will enhance the quality and reliability of this new sample.

@eapl-gemugami eapl-gemugami added the snippet-bot:force-run Force snippet-bot runs its logic label Jun 23, 2025
@snippet-bot snippet-bot bot removed the snippet-bot:force-run Force snippet-bot runs its logic label Jun 23, 2025
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This PR requires new permissions for the google_ml_integration as in Grant Vertex AI user permission to AlloyDB service agent

Kokoro CI - Python 3.9 Log:

pg8000.exceptions.DatabaseError:
{
  'S': 'ERROR',
  'V': 'ERROR',
  'C': 'GAV07',
  'M': 'Permission denied on the resource.',
  'D': 'Prediction request failed with \n HTTPError[403]: {\n  "error": {\n    "code": 403,\n    "message": "Permission \'aiplatform.endpoints.predict\' denied on resource \'//[aiplatform.googleapis.com/projects/1012616486416/locations/us-central1/publishers/google/models/text-embedding-005\](http://aiplatform.googleapis.com/projects/1012616486416/locations/us-central1/publishers/google/models/text-embedding-005%5C)' (or it may not exist).",\n    "status": "PERMISSION_DENIED",\n    "details": [\n      {\n        "@type": "[type.googleapis.com/google.rpc.ErrorInfo](http://type.googleapis.com/google.rpc.ErrorInfo)",\n        "reason": "IAM_PERMISSION_DENIED",\n        "domain": "[aiplatform.googleapis.com](http://aiplatform.googleapis.com/)",\n        "metadata": {\n          "resource": "projects/1012616486416/locations/us-central1/publishers/google/models/text-embedding-005",\n          "permission": "aiplatform.endpoints.predict"\n        }\n      }\n    ]\n  }\n}\n',
    'H': 'Verify and set required permissions to secret manager, Vertex AI endpoint and/ or third party models.',
  'W': 'SQL statement "SELECT ml_predict_row(FORMAT(\'publishers/google/models/%s\', model_id), json_build_object(\'instances\', json_build_array(json_build_object(\'content\', content))))"\nPL/pgSQL function google_ml._call_vertex_embedding(text,text) line 6 at SQL statement\nPL/pgSQL function embedding(text,text) line 13 at RETURN',
  'F': 'google_ml_integration.cc',
  'L': '397',
  'R': 'ml_predict_row_impl'
}

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