Add vector_search function for pipeline aggregation #30
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| def update_one(self, collection, query, update): | ||
| return self.db[collection].update_one(query, update) | ||
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| # add a function for pipeline aggregation vector search | ||
| def vector_search(self, collection, embedding): | ||
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| result = self.db[collection].aggregate([ | ||
| { | ||
| "$vectorSearch": { | ||
| "index": "vector_index", | ||
| "path": "face_embedding", | ||
| "queryVector": embedding, | ||
| "numCandidates": 20, | ||
| "limit": 20 | ||
| } | ||
| }, { | ||
| '$project': { | ||
| '_id': 0, | ||
| 'Name': 1, | ||
| 'Image': 1, | ||
| 'score': { | ||
| '$meta': 'vectorSearchScore' | ||
| } | ||
| } | ||
| } | ||
| ]) | ||
| result_arr = [i for i in result] | ||
| return result_arr |
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Consider extracting the query and projection into variables for better readability and maintainability. This practice enhances code clarity and simplifies future modifications.
def vector_search(self, collection, embedding):
query = {
"$vectorSearch": {
"index": "vector_index",
"path": "face_embedding",
"queryVector": embedding,
"numCandidates": 20,
"limit": 20
}
}
projection = {
'$project': {
'_id': 0,
'Name': 1,
'Image': 1,
'score': {'$meta': 'vectorSearchScore'}
}
}
result = self.db[collection].aggregate([query, projection])
return [i for i in result]
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CHANGELOG.md
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| - Resolved various bugs and issues identified during the testing process. | ||
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| ### Removed | ||
| - Removed deprecated code and unused dependencies from the project. No newline at end of file | ||
| - Removed deprecated code and unused dependencies from the project. | ||
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| ## [0.1.4] - 2024-03-16 - 23:00 | ||
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| ### Added | ||
| - Implemented a new `recognize_Face` endpoint in [`route.py`](API/route.py). This endpoint accepts a base64 string as input, converts it into embeddings, and performs a vector search query on the MongoDB Atlas database. Changes made by @Devasy23. | ||
| - Added a new `vector_search` function in [`database.py`](API/database.py). This function performs a vector similarity search on the MongoDB Atlas database using Euclidean distance as the similarity measure. Changes made by @Devasy23. | ||
| - Updated [`index.ipynb`](index.ipynb) to include examples and usage of the new `recognize_Face` endpoint and `vector_search` function. Changes made by @Devasy23. | ||
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| ### Changed | ||
| - Updated the `Database` class in [`database.py`](API/database.py) to include the new `vector_search` function. Changes made by @Devasy23. | ||
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| ### Fixed | ||
| - Resolved various bugs and issues identified during the implementation and testing of the new features. Fixes made by @Devasy23. |
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Great work on extending the project's functionality with the new recognize_Face endpoint and vector_search function. Here's a little feedback:
- Code Reusability and Clarity: For the
vector_searchfunction, consider defining a separate utility for Euclidean distance calculation if not done already. This promotes code reuse, especially if other parts of the system perform similar computations.
# Suggested improvement for database.py
def euclidean_distance(vector1, vector2):
return np.linalg.norm(vector1-vector2)
def vector_search(query_vector):
# Use euclidean_distance in the search algorithm.-
Documentation and Examples: Ensure the updated
index.ipynbhas clear, concise examples that not only illustrate how to use the new features but also highlight any potential edge cases or limitations. -
Consistent Formatting: The addition of a newline at the end of files is a good practice; make sure this is consistently applied across all edited files.
Remember to consider unit tests for the new functionalities if not already included, ensuring robustness and future maintainability.
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API/route.py
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| client.find_one_and_delete(collection, {"EmployeeCode": EmployeeCode}) | ||
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| return {"Message": "Successfully Deleted"} | ||
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| @router.post("/recognize_face", response_class=Response) | ||
| async def recognize_face(Face: UploadFile = File(...)): | ||
| """ | ||
| Recognize a face from the provided image. | ||
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| Args: | ||
| Face (UploadFile): The image file to be recognized. | ||
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| Returns: | ||
| Response: A response object containing the recognized employee information in JSON format. | ||
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| Raises: | ||
| HTTPException: If an internal server error occurs. | ||
| """ | ||
| logging.info("Recognizing Face") | ||
| try: | ||
| img_data = await Face.read() | ||
| with open("temp.png", "wb") as f: | ||
| f.write(img_data) | ||
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| embedding = DeepFace.represent(img_path="temp.png", model_name="Facenet") | ||
| result = client2.vector_search(collection2, embedding[0]['embedding']) | ||
| logging.info(f"Result: {result}") | ||
| os.remove("temp.png") | ||
| except Exception as e: | ||
| logging.error(f"Error: {e}") | ||
| os.remove("temp.png") | ||
| raise HTTPException(status_code=500, detail="Internal server error") | ||
| return Response( | ||
| content=bytes(json.dumps(result[0], default=str), "utf-8"), | ||
| media_type="application/json", | ||
| ) |
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- Temporary File Creation: Directly writing the uploaded image to a file (
temp.png) can lead to concurrency issues and security concerns. Use a temporary file with a context manager to ensure it gets cleaned up properly, even in case of errors.
from tempfile import NamedTemporaryFile
async def recognize_face(Face: UploadFile = File(...)):
logging.info("Recognizing Face")
try:
img_data = await Face.read()
with NamedTemporaryFile(delete=True, suffix=".png") as temp_file:
temp_file.write(img_data)
temp_file.flush()
embedding = DeepFace.represent(img_path=temp_file.name, model_name="Facenet")
result = client2.vector_search(collection2, embedding[0]['embedding'])
except Exception as e:
logging.error(f"Error: {e}")
raise HTTPException(status_code=500, detail="Internal server error")-
Error Handling: Current error handling might catch too broad of a range of exceptions, potentially swallowing unexpected errors and making debugging difficult. Be specific about which errors you catch or ensure to re-raise unexpected ones.
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File Reading Directly in Endpoint: It's a good practice to separate out logic into service layers or utility functions. This aids in keeping your endpoint functions clean and more maintainable.
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Use Environment Variables for file paths or model names to make the application more flexible and secure.
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DRY Principle: Consider whether the pattern of removing a file is repeated elsewhere in your code. If so, abstract the cleanup logic into a utility function.
Overall, ensure every aspect adheres to scalability, security, and maintainability principles.
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This pull request adds a new function called
vector_searchto theDatabaseclass indatabase.py. Thevector_searchfunction performs a pipeline aggregation vector search on the MongoDB Atlas database using the provided embedding. It returns a list of results with the name, image, and score of the closest matches. This functionality is useful for performing similarity searches based on face embeddings.