Feature/langchain agent #3
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Summary of Changes
Hello @yamamomo2022, 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 significantly expands the project's capabilities by integrating advanced AI agent functionality via Google Vertex AI Langchain and geospatial data processing using Google Earth Engine. These additions enable the system to leverage powerful language models for complex queries and generate Normalized Difference Vegetation Index (NDVI) images from satellite data, enhancing both intelligent automation and environmental analysis features.
Highlights
- Vertex AI Agent Integration: A new module
functions/agent.pyhas been added to demonstrate the setup and querying of a Vertex AI Langchain agent, including model configuration and safety settings. - Google Earth Engine NDVI Generation: A new module
functions/gee_ndvi.pyis introduced to handle authentication with Google Earth Engine, retrieve Sentinel-2 data, calculate NDVI, and save the resulting image for specified locations and date ranges. - Dependency Management: The
earthengine-apidependency has been added tofunctions/requirements.txtto support the new Google Earth Engine functionalities. - Documentation Update: The
README.mdfile now includes a reference link to the Vertex AI Langchain agent documentation.
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Pull Request Overview
This pull request introduces Google Vertex AI Agent and Google Earth Engine NDVI image generation capabilities to the Firebase functions project, along with necessary dependency updates and documentation references.
- Added Vertex AI Langchain agent integration with configurable model parameters and safety settings
- Implemented Google Earth Engine NDVI image generation functionality for satellite data processing
- Updated project dependencies and documentation to support the new features
Reviewed Changes
Copilot reviewed 4 out of 4 changed files in this pull request and generated 4 comments.
| File | Description |
|---|---|
| functions/requirements.txt | Added earthengine-api dependency for Google Earth Engine operations |
| functions/gee_ndvi.py | New module for GEE authentication, NDVI calculation, and image generation |
| functions/agent.py | New module for Vertex AI Langchain agent setup and querying |
| README.md | Added reference link to Vertex AI Langchain agent documentation |
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| init(project="ai-agent-hackathon-3-471422", location="us-central1") |
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Hard-coded project ID should be moved to environment variables or configuration file to avoid exposing sensitive project information in source code.
| init(project="ai-agent-hackathon-3-471422", location="us-central1") | |
| import os | |
| init( | |
| project=os.environ.get("VERTEXAI_PROJECT_ID"), | |
| location="us-central1" | |
| ) |
| .filterBounds(point) \ | ||
| .filterDate(start_date, end_date) \ | ||
| .sort('CLOUD_COVER') | ||
| image = collection.first() |
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The code assumes at least one image exists in the collection, but if no Sentinel-2 images are found for the specified location and date range, collection.first() will return null, causing subsequent operations to fail.
| image = collection.first() | |
| image = collection.first() | |
| if image is None: | |
| raise ValueError("No Sentinel-2 images found for the specified location and date range.") |
| # 可視化パラメータ | ||
| vis_params = {'min': 0, 'max': 1, 'palette': ['blue', 'white', 'green']} | ||
| # 画像をエクスポート | ||
| url = ndvi.getThumbURL({'region': point.buffer(10000).bounds().getInfo(), 'dimensions': 512, 'format': 'png', **vis_params}) |
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The buffer distance (10000) and image dimensions (512) are magic numbers that should be defined as named constants or function parameters for better maintainability.
| # 画像をエクスポート | ||
| url = ndvi.getThumbURL({'region': point.buffer(10000).bounds().getInfo(), 'dimensions': 512, 'format': 'png', **vis_params}) | ||
| # 画像ダウンロード | ||
| r = requests.get(url) |
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The HTTP request lacks error handling. If the request fails or returns a non-200 status code, the function will still attempt to write the response content to file, potentially creating invalid image files.
| r = requests.get(url) | |
| r = requests.get(url) | |
| try: | |
| r.raise_for_status() | |
| except requests.RequestException as e: | |
| raise RuntimeError(f"Failed to download NDVI image: {e}") |
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Thank you for the PR adding Vertex AI Agent and Google Earth Engine NDVI functionality. Here are the key points from the review:
Security:
- Hardcoded project ID in agent.py should be moved to environment variables
- File path validation needed in gee_ndvi.py to prevent path traversal
- Consider strengthening content safety settings in the Vertex AI agent
Code Quality:
- Error handling in GEE initialization needs to be more specific
- Dependencies should be version-pinned for reproducibility
Please address these issues, particularly the security-related ones, before merging. The code suggestions provided should help with implementing the fixes.
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| init(project="ai-agent-hackathon-3-471422", location="us-central1") |
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🛑 [Security Risk]: The project ID is hardcoded in the code. This is a sensitive configuration that should be managed through environment variables or a secure configuration system1.
| init(project="ai-agent-hackathon-3-471422", location="us-central1") | |
| from os import getenv | |
| from vertexai import agent_engines, init | |
| init(project=getenv("GOOGLE_CLOUD_PROJECT"), location=getenv("GOOGLE_CLOUD_LOCATION", "us-central1")) |
Footnotes
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CWE-798: Use of Hard-coded Credentials - https://cwe.mitre.org/data/definitions/798.html ↩
| HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE, | ||
| HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_ONLY_HIGH, | ||
| HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE, | ||
| HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE, |
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Consider setting SEXUALLY_EXPLICIT content blocking to at least BLOCK_MEDIUM_AND_ABOVE for better content safety. The current BLOCK_NONE setting might allow inappropriate content through.
| try: | ||
| ee.Initialize() | ||
| except Exception: |
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🛑 [Error Handling]: The exception handling is too broad. Catching all exceptions can mask serious issues. Consider catching specific exceptions and adding proper error logging.
| try: | |
| ee.Initialize() | |
| except Exception: | |
| def initialize_gee(): | |
| """認証と初期化。ローカル認証が必要な場合はee.Authenticate()を使う。""" | |
| try: | |
| ee.Initialize() | |
| except ee.EEException as e: | |
| logging.info("Initial initialization failed, attempting authentication") | |
| try: | |
| ee.Authenticate() | |
| ee.Initialize() | |
| except ee.EEException as e: | |
| logging.error(f"Failed to initialize Google Earth Engine: {e}") | |
| raise |
| r = requests.get(url) | ||
| with open(out_path, 'wb') as f: |
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🛑 [Security Risk]: Writing files without path validation could lead to path traversal vulnerabilities1. Add validation for the out_path parameter.
| r = requests.get(url) | |
| with open(out_path, 'wb') as f: | |
| # Validate and sanitize file path | |
| safe_path = os.path.abspath(os.path.normpath(out_path)) | |
| if not safe_path.endswith('.png'): | |
| raise ValueError("Output path must end with .png") | |
| with open(safe_path, 'wb') as f: | |
| f.write(r.content) |
Footnotes
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CWE-22: Improper Limitation of a Pathname to a Restricted Directory - https://cwe.mitre.org/data/definitions/22.html ↩
| @@ -1 +1,2 @@ | |||
| firebase_functions~=0.1.0 | |||
| firebase_functions~=0.1.0 | |||
| earthengine-api | |||
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Consider pinning dependency versions for better reproducibility and security. Unpinned dependencies can lead to unexpected behavior or vulnerabilities if a package releases breaking changes.
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Code Review
This pull request introduces new features for Vertex AI and Google Earth Engine. The code is generally well-structured, but there are several areas for improvement. My review focuses on enhancing robustness and maintainability by addressing hardcoded configuration, improving error handling for both application logic and network requests, and ensuring reproducible dependencies. These changes will make the code more reliable and easier to manage in different environments.
| from vertexai import agent_engines, init | ||
| from langchain_google_vertexai import HarmBlockThreshold, HarmCategory | ||
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| init(project="ai-agent-hackathon-3-471422", location="us-central1") |
There was a problem hiding this comment.
Hardcoding the project ID and location can lead to issues when deploying to different environments (e.g., staging, production) and is a security risk if the repository is public. It's better to load these values from environment variables. You will need to add import os at the top of the file.
| init(project="ai-agent-hackathon-3-471422", location="us-central1") | |
| init(project=os.environ.get("GCP_PROJECT"), location=os.environ.get("GCP_LOCATION", "us-central1")) |
| image = collection.first() | ||
| # NDVI計算 | ||
| ndvi = image.normalizedDifference(['B8', 'B4']).rename('NDVI') |
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The collection.first() call might return None if no images are found for the given location and date range. This would cause an AttributeError when image.normalizedDifference is called. You should add a check to handle this case gracefully.
| image = collection.first() | |
| # NDVI計算 | |
| ndvi = image.normalizedDifference(['B8', 'B4']).rename('NDVI') | |
| image = collection.first() | |
| if not image: | |
| raise ValueError("No image found for the specified criteria.") | |
| ndvi = image.normalizedDifference(['B8', 'B4']).rename('NDVI') |
| r = requests.get(url) | ||
| with open(out_path, 'wb') as f: | ||
| f.write(r.content) |
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The requests.get() call does not handle potential HTTP errors (e.g., 4xx or 5xx status codes). If the URL fetch fails, r.content might be an error page. It's good practice to check the response status. A simple way is to call r.raise_for_status() after the request, which will raise an HTTPError for bad responses.
| r = requests.get(url) | |
| with open(out_path, 'wb') as f: | |
| f.write(r.content) | |
| r = requests.get(url) | |
| r.raise_for_status() | |
| with open(out_path, 'wb') as f: | |
| f.write(r.content) |
| """認証と初期化。ローカル認証が必要な場合はee.Authenticate()を使う。""" | ||
| try: | ||
| ee.Initialize() | ||
| except Exception: |
There was a problem hiding this comment.
Catching a generic Exception is too broad and can hide other unexpected issues. It's better to catch the specific exception that ee.Initialize() might raise, which is ee.ee_exception.EEException. This makes the error handling more robust and explicit.
| except Exception: | |
| except ee.ee_exception.EEException: |
| # 可視化パラメータ | ||
| vis_params = {'min': 0, 'max': 1, 'palette': ['blue', 'white', 'green']} | ||
| # 画像をエクスポート | ||
| url = ndvi.getThumbURL({'region': point.buffer(10000).bounds().getInfo(), 'dimensions': 512, 'format': 'png', **vis_params}) |
There was a problem hiding this comment.
The value 10000 (presumably meters for the buffer) is a magic number. It's better to define it as a constant with a descriptive name at the top of the function or module, like REGION_BUFFER_METERS = 10000. This improves readability and makes it easier to change the value if needed.
| @@ -1 +1,2 @@ | |||
| firebase_functions~=0.1.0 | |||
| firebase_functions~=0.1.0 | |||
| earthengine-api | |||
There was a problem hiding this comment.
It's a good practice to pin dependency versions to ensure reproducible builds and avoid unexpected breakages from upstream changes. Please specify a version for earthengine-api, for example earthengine-api==0.1.393 (the latest version as of writing).
earthengine-api==0.1.393
This pull request introduces two new Python modules for integrating Google Vertex AI Agent and Google Earth Engine NDVI image generation, along with the necessary dependency updates. It also adds a reference link to the documentation in the
README.md. The most important changes are grouped below:Vertex AI Agent Integration:
functions/agent.pyto demonstrate how to set up and query a Vertex AI Langchain agent, including model configuration and safety settings.Google Earth Engine NDVI Image Generation:
functions/gee_ndvi.pyfor authenticating with Google Earth Engine, retrieving Sentinel-2 data, calculating NDVI, and saving the resulting image for a specified location and date range.functions/requirements.txtto include theearthengine-apidependency required for Google Earth Engine operations.Documentation:
README.md.