feat: add support for openai embedding #23
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WalkthroughThe changes introduced in this pull request encompass several updates across various files in the backend of the application. Key modifications include the addition of new configuration variables in the Changes
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Actionable comments posted: 7
🧹 Outside diff range and nitpick comments (12)
backend/Makefile (1)
4-4: Approved: Enhanced logging for better debugging and monitoring.The addition of
--log-level infoto the Uvicorn command is a good improvement. It will provide more detailed logs during development and testing, which can be crucial when integrating new features like OpenAI embedding support.Consider making the log level configurable through an environment variable. This would allow for easy adjustment of log verbosity without modifying the Makefile. For example:
run: poetry run uvicorn app.main:app --reload --port 5328 --log-level ${LOG_LEVEL:-info}This way, you can set a different log level by running
LOG_LEVEL=debug make runor similar.backend/app/database/__init__.py (3)
9-9: Consider using a configuration-based approach for the verbose flag.The logger initialization looks good, but setting
verbose=Truedirectly in the code might lead to excessive logging in production environments. Consider using a configuration variable to control the verbosity level, allowing for easier management across different environments.You could modify this line to use a configuration variable:
logger = Logger(verbose=settings.DEBUG)This assumes you have a
DEBUGflag in your settings. If not, you might want to add one.
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1-51: Summary: New logging mechanism introduced.The changes in this file introduce a new logging mechanism using a custom
Loggerclass. While the implementation is correct, there are a few points to consider:
- The
verbose=Trueflag is hardcoded, which might lead to excessive logging in production environments.- The new logger is used in the
_add_filtering_criteriafunction, which could significantly increase log volume.- These changes might have a broader impact on the application's logging behavior and performance.
To ensure a smooth transition:
- Consider using a configuration-based approach for controlling verbosity.
- Review the logging strategy across the entire application for consistency.
- Monitor the application's performance and log volume after deploying these changes.
Increased Logging Verbosity Confirmed
Logger instances with
verbose=Trueare present in:
backend/app/vectorstore/chroma.pybackend/app/database/__init__.pyAdditionally, multiple
logger.debugcalls are found in:
backend/app/logger.pybackend/app/database/__init__.pyThis confirms that the recent changes lead to more detailed logging, which may significantly increase log volume in production environments.
🔗 Analysis chain
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30-51: New logger usage in _add_filtering_criteria function.The function now uses the new logger for debug statements. This change is consistent with the logger update and provides more detailed logging. However, be aware that this might significantly increase log volume, especially if
verbose=Trueis used in production.To ensure the impact of these changes is well understood, let's verify the logger usage across the codebase:
This script will help us understand the extent of the logger changes and identify any inconsistencies in logger usage across the codebase.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for logger usage and potential verbose flags across the codebase # Test 1: Check for Logger instantiations echo "Logger instantiations:" rg --type python "Logger\(.*verbose.*\)" # Test 2: Check for debug log calls echo "\nDebug log calls:" rg --type python "logger\.debug" # Test 3: Check for any remaining uses of the standard logging module echo "\nStandard logging usage:" rg --type python "import logging|from logging import"Length of output: 389
Script:
#!/bin/bash # Description: Check for logger usage and potential verbose flags across the codebase # Test 1: Check for Logger instantiations echo "Logger instantiations:" rg --type py "Logger\(.*verbose.*\)" # Test 2: Check for debug log calls echo "\nDebug log calls:" rg --type py "logger\.debug" # Test 3: Check for any remaining uses of the standard logging module echo "\nStandard logging usage:" rg --type py "import logging|from logging import"Length of output: 815
backend/tests/vectorstore/test_chroma_db.py (1)
24-38: LGTM with suggestion: Custom batch size test is good, but could be more thorough.This test effectively verifies that the
add_docsmethod respects the custom batch size by checking the number of calls to the mocked_docs_collection.addmethod.Consider enhancing this test by also verifying the content of each batch passed to the
addmethod. This would ensure not only that the correct number of batches are processed, but also that each batch contains the expected documents, ids, and metadata. You could achieve this by adding assertions on the call arguments of the mockedaddmethod.backend/test.py (1)
1-127: Overall improvements needed for the test scriptThis test script covers important functionality including database interactions, file preprocessing, and vector store operations. However, there are several areas that need improvement to make it a robust and maintainable test suite:
- Remove comments and use proper testing framework (e.g., pytest) instead of a standalone script.
- Implement secure handling of sensitive information like API keys.
- Use proper logging mechanisms instead of print statements.
- Add comprehensive error handling for all operations.
- Clarify the purpose and improve the structure of each test section.
- Move hardcoded test data to separate files or use data generation utilities.
- Implement proper benchmarking for performance-critical operations.
- Add assertions to validate the results of each operation.
Consider restructuring this script into multiple test files, each focusing on a specific component (e.g., database operations, file preprocessing, vector store operations). This will improve maintainability and allow for more focused testing of each component.
Would you like assistance in refactoring this script into a proper test suite using pytest? I can help create a basic structure and provide examples for each component.
backend/app/logger.py (2)
83-92: LGTM: Thedebugmethod is well-implemented. Consider refactoring to reduce duplication.The new
debugmethod correctly logs messages at the DEBUG level and maintains consistency with the existing logging structure. It properly utilizes the helper methods for calculating time differences and determining the source of invocation.However, there's significant code duplication between the
info,debug, anderrormethods. Consider refactoring these methods to reduce duplication and improve maintainability.Here's a suggested refactoring to reduce code duplication:
def _log_message(self, message: str, level: int): log_func = getattr(self._logger, logging.getLevelName(level).lower()) log_func(message) self._logs.append( { "msg": message, "level": logging.getLevelName(level), "time": self._calculate_time_diff(), "source": self._invoked_from(), } ) def info(self, message: str): self._log_message(message, logging.INFO) def debug(self, message: str): self._log_message(message, logging.DEBUG) def error(self, message: str): self._log_message(message, logging.ERROR)This refactoring introduces a private
_log_messagemethod that handles the common logging logic, reducing duplication across theinfo,debug, anderrormethods.
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39-57: Consider refactoring thelogmethod for consistency.The
logmethod handles multiple log levels, while the newinfoanddebugmethods (and the existingerrormethod) are level-specific. To improve consistency and maintainability, consider refactoring thelogmethod to use the same pattern as the level-specific methods.Here's a suggested refactoring for the
logmethod:def log(self, message: str, level: int = logging.INFO): log_func = getattr(self._logger, logging.getLevelName(level).lower()) log_func(message) self._logs.append( { "msg": message, "level": logging.getLevelName(level), "time": self._calculate_time_diff(), "source": self._invoked_from(), } )This refactoring aligns the
logmethod with the structure of the level-specific methods, improving overall consistency in the class.backend/app/api/v1/projects.py (4)
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182-183: Ensure Secure Filename Handling to Prevent Security VulnerabilitiesCurrently, the code replaces spaces with underscores in filenames but does not sanitize the filename for other potentially dangerous characters. This could lead to security issues like path traversal attacks or file overwrites.
Consider using a secure method to sanitize filenames, such as the
secure_filenamefunction fromwerkzeug.utils:from werkzeug.utils import secure_filename # ... filename = secure_filename(file.filename) filepath = os.path.join(settings.upload_dir, str(id), filename)This ensures that the filename is safe and does not contain any harmful characters.
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207-207: Optimize Directory Creation Outside the LoopThe upload directory creation is currently inside the loop, which means it attempts to create the same directory multiple times unnecessarily.
Move the directory creation outside the loop:
# Before the loop os.makedirs(os.path.join(settings.upload_dir, str(id)), exist_ok=True) # Inside the loop, remove the directory creation - os.makedirs(os.path.join(settings.upload_dir, str(id)), exist_ok=True)This avoids redundant operations and improves efficiency.
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209-220: Improve Error Handling for Multiple URLsRaising an
HTTPExceptionimmediately upon encountering an invalid URL stops the processing of any subsequent URLs. It's better to collect all invalid URLs and process the valid ones.Suggested modification:
invalid_urls = [] valid_urls = [] for url in urls: if not is_valid_url(url): invalid_urls.append(url) else: valid_urls.append(url) if invalid_urls: raise HTTPException( status_code=400, detail=f"Invalid URL(s): {', '.join(invalid_urls)}. Please provide valid URL(s).", ) # Proceed to process valid URLs for url in valid_urls: # Processing logic...This approach provides users with comprehensive feedback on all invalid URLs at once.
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237-247: Set Dynamic Media Type Based on File ContentIn the
get_filefunction, the media type is hardcoded toapplication/pdf. Since assets may be of different types (e.g., HTML files from URL assets), determine the media type dynamically.Modify the code to use the
mimetypesmodule:import mimetypes # ... media_type, _ = mimetypes.guess_type(asset.filename) media_type = media_type or "application/octet-stream" return FileResponse( filepath, media_type=media_type, filename=asset.filename )This ensures the correct content type is set for various file types.
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📒 Files selected for processing (9)
- backend/.env.example (1 hunks)
- backend/Makefile (1 hunks)
- backend/app/api/v1/projects.py (1 hunks)
- backend/app/config.py (1 hunks)
- backend/app/database/init.py (1 hunks)
- backend/app/logger.py (1 hunks)
- backend/app/vectorstore/chroma.py (3 hunks)
- backend/test.py (1 hunks)
- backend/tests/vectorstore/test_chroma_db.py (1 hunks)
✅ Files skipped from review due to trivial changes (1)
- backend/.env.example
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🔇 Additional comments (20)
backend/Makefile (3)
9-11: Approved: Improved Makefile structure and consistency.The reformatting of the
generate-migrationtarget and the addition of the.PHONYdeclaration are good improvements:
- The adjusted whitespace enhances readability.
- The
.PHONYdeclaration is a best practice in Makefiles, ensuring that the target always runs and preventing potential conflicts with files of the same name.These changes contribute to a more maintainable and robust Makefile.
14-14: Approved: Added newline at end of file.Adding a newline at the end of the file is a good practice. It ensures compatibility with various tools and version control systems that expect files to end with a newline character. This small change contributes to maintaining clean and consistent code.
1-14: Overall assessment: Positive improvements to the Makefile.The changes made to this Makefile are all positive improvements:
- Enhanced logging in the
runtarget will aid in debugging and monitoring, especially useful for the new OpenAI embedding support.- Better structure and consistency with the
.PHONYdeclaration and reformatting.- Proper file formatting with the added newline at the end.
These changes contribute to a more robust and maintainable development environment. Good job!
backend/app/config.py (2)
22-22: Please provide more context forchroma_batch_size.The addition of
chroma_batch_sizeseems related to ChromaDB operations. Could you please clarify:
- The specific purpose of this variable in the context of the application?
- The reasoning behind the default value of 5?
- Whether this value should be configurable via environment variables?
This information will help ensure the variable is appropriately set and documented.
21-21: Approve renaming, but verify impact on existing code.The renaming of
MAX_FILE_SIZEtomax_file_sizeimproves consistency with Python naming conventions. However, ensure that this change doesn't break existing code that might be referencing the old name.Run the following script to check for any remaining usage of the old variable name:
✅ Verification successful
Issue with previous search command.
The shell script to search for
MAX_FILE_SIZEfailed becausepythonis not a recognized file type in the current environment. Please use the following corrected script to search forMAX_FILE_SIZEin Python files:
Re-attempting search for
MAX_FILE_SIZEin Python files.Please use the following corrected script to search for any remaining usage of
MAX_FILE_SIZEwithin Python files:
Re-attempting search for
MAX_FILE_SIZEin Python files.Please use the following script to search for any remaining usage of
MAX_FILE_SIZEwithin Python files:
No remaining usages of
MAX_FILE_SIZEfound.The renaming of
MAX_FILE_SIZEtomax_file_sizedoes not break any existing code references.🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Search for any remaining usage of MAX_FILE_SIZE in the codebase. # Test: Search for MAX_FILE_SIZE. Expect: No results if all occurrences have been updated. rg --type python 'MAX_FILE_SIZE'Length of output: 63
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#!/bin/bash # Description: Search for any remaining usage of MAX_FILE_SIZE in Python files. rg 'MAX_FILE_SIZE' -- '*.py'Length of output: 73
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#!/bin/bash # Description: Search for any remaining usage of MAX_FILE_SIZE in Python files. # Find all Python files and search for 'MAX_FILE_SIZE' fd --extension py --type f | xargs rg 'MAX_FILE_SIZE'Length of output: 53
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#!/bin/bash # Description: Search for any remaining usage of MAX_FILE_SIZE in Python files. rg 'MAX_FILE_SIZE' --glob '*.py'Length of output: 32
backend/app/database/__init__.py (1)
7-7: LGTM: New logger import.The import statement for the custom Logger class is correct and aligns with the introduction of a new logging mechanism.
backend/tests/vectorstore/test_chroma_db.py (7)
1-12: LGTM: Imports and fixture setup look good.The imports are appropriate for the testing context, and the
mock_settingsfixture correctly sets up the environment variables needed for isolated testing. Good use of dependency injection by returning aSettingsinstance.
14-17: LGTM: Initialization test is well-structured.This test function effectively verifies the correct initialization of ChromaDB, checking both the batch size and the type of embedding function used. The assertions are clear and appropriate.
19-22: LGTM: OpenAI embeddings test is correct.This test function properly verifies that ChromaDB uses the OpenAIEmbeddingFunction when OpenAI embeddings are enabled in the settings. The test setup and assertion are appropriate.
40-58: LGTM: OpenAI embeddings add_docs test is thorough.This test function effectively verifies the behavior of the
add_docsmethod when OpenAI embeddings are used. It correctly checks that the_docs_collection.addmethod is called once with the expected parameters. The use ofassert_called_once_withis a good practice for verifying both the call count and the arguments in a single assertion.
60-61: LGTM: Max file size setting test is correct.This test function effectively verifies that the
max_file_sizesetting is correctly set to 20 MB. The use of a clear calculation (20 * 1024 * 1024) makes the expected value easy to understand.
63-64: LGTM: Chroma batch size setting test is correct.This test function effectively verifies that the
chroma_batch_sizesetting is correctly set to 10. The comment clarifying that this value is set by themock_settingsfixture is helpful for understanding the test setup.
1-64: Overall: Excellent test suite for ChromaDB.This test file provides a comprehensive set of unit tests for the ChromaDB class. It effectively covers various aspects of the class's functionality, including initialization, embedding types, document addition with different configurations, and settings verification. The use of pytest fixtures and mocking is appropriate and contributes to isolated and reliable tests.
Key strengths:
- Good coverage of different scenarios and configurations.
- Effective use of mocking for isolating tests.
- Clear and descriptive test names.
- Appropriate assertions for each test case.
Minor suggestion for improvement:
- In the
test_add_docs_with_custom_batch_sizefunction, consider adding assertions to verify the content of each batch passed to the mockedaddmethod.Overall, this test suite provides a solid foundation for ensuring the reliability and correctness of the ChromaDB class.
backend/test.py (2)
41-42: 🛠️ Refactor suggestionClarify file preprocessing and add error handling
The purpose and context of the
preprocess_filefunction call are not clear. Additionally, there's no error handling or logging for this operation.Please provide more context about the
preprocess_filefunction and its purpose in this test script.Consider adding error handling and logging:
try: preprocessed_file = preprocess_file(167) logger.info(f"File preprocessing completed. Result: {preprocessed_file}") except Exception as e: logger.error(f"Error during file preprocessing: {str(e)}")This will help in debugging and understanding the flow of the test script.
44-103: 🛠️ Refactor suggestionImprove vector store setup and clarify its purpose
The current implementation uses a large list of hardcoded example sentences, which is not ideal for maintainability and readability. Additionally, the purpose of this vector store setup in the context of the test script is not clear.
- Consider moving the example sentences to a separate file or using a data generation library.
- Add comments or documentation explaining the purpose of this vector store setup in the test script.
Example refactoring:
from app.utils.test_data import get_example_sentences # Create this utility function # Get example sentences and metadata docs = get_example_sentences() metadata = [{"source": f"source{i}"} for i in range(len(docs))] # Initialize ChromaDB vector store vectorstore = ChromaDB("panda-etl-TESTc")Could you please clarify the purpose of this vector store setup in the context of the test script? This will help in providing more specific recommendations for improvement.
backend/app/logger.py (2)
72-81: LGTM: Theinfomethod is well-implemented.The new
infomethod correctly logs messages at the INFO level and maintains consistency with the existing logging structure. It properly utilizes the helper methods for calculating time differences and determining the source of invocation.
72-92: Overall assessment: Good additions with room for improvement.The new
infoanddebugmethods are well-implemented and enhance the logging capabilities of theLoggerclass. They maintain consistency with the existing structure and correctly utilize helper methods.To further improve the code:
- Consider refactoring the level-specific methods (
info,debug,error) to reduce code duplication.- Align the
logmethod with the structure of the level-specific methods for better consistency.These refactorings would improve maintainability and readability without changing the functionality.
backend/app/vectorstore/chroma.py (1)
53-53: Verify if 'self.settings.chromadb_url' is the correct attribute for 'persist_directory'At line 53,
persist_directoryis set toself.settings.chromadb_url. Please confirm ifchromadb_urlis the correct attribute to use here, or if it should bechromadb_persist_directoryor another relevant setting. This ensures that the ChromaDB client points to the correct directory for persistence.Run the following script to check the definition and usage of
chromadb_urlin the codebase:✅ Verification successful
Verification Complete: 'chromadb_url' is correctly used for 'persist_directory'
No issues found.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Search for the definition of 'chromadb_url' in the settings module. # Test: Find where 'chromadb_url' is defined. Expect: It should be defined in `app/config/settings.py` with the correct path. rg --type python 'chromadb_url' app/config/Length of output: 74
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#!/bin/bash # Description: Search for the definition of 'chromadb_url' in Python files within the settings module. # Test: Find where 'chromadb_url' is defined. Expect: It should be defined in `app/config/settings.py` with the correct path. rg -g "*.py" 'chromadb_url' app/config/Length of output: 91
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#!/bin/bash # Description: Recursively search for 'chromadb_url' in all Python files within the repository. # Test: Locate all instances and definitions of 'chromadb_url' to verify its correct usage. rg -g "*.py" 'chromadb_url'Length of output: 197
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#!/bin/bash # Description: Extract the definition and assignment of 'chromadb_url' in backend/app/config.py. # Test: Confirm the value assigned to 'chromadb_url' to verify its correctness for 'persist_directory'. ast-grep --lang python --pattern 'chromadb_url: str = $_' backend/app/config.pyLength of output: 264
backend/app/api/v1/projects.py (2)
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264-275: Confirm Consistent Error Handling in Asset DeletionIn the
delete_assetfunction, if an error occurs while deleting from the vector store, the code logs the error but continues to soft delete the asset without notifying the user of the partial failure.Consider whether it's appropriate to proceed without informing the user or if an error message should be returned. Run the following script to check for existing error handling patterns:
#!/bin/bash # Description: Search for error handling patterns in 'delete_asset' functions. ast-grep --lang python --pattern $'def delete_asset($$):\n $$$' backend/app/api/v1/projects.pyReview the function implementations to ensure consistent and user-friendly error handling.
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202-204: Verify That 'generate_unique_filename' Properly Sanitizes FilenamesWhen generating filenames from URLs in the
add_url_assetfunction, it's crucial to ensure that the filenames are properly sanitized to prevent security issues like path traversal.Run the following script to inspect the
generate_unique_filenamefunction:This script locates the function definition and prints its implementation so you can verify that it handles filename sanitization appropriately.
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Actionable comments posted: 2
🧹 Outside diff range and nitpick comments (3)
backend/app/vectorstore/chroma.py (3)
30-31: Enhanced configurability with new parameters and settings.The addition of
batch_sizeandsettingsparameters, along with the use ofself.settings, improves the flexibility and configurability of theChromaDBclass. This change allows for better customization of the class behavior.Consider simplifying the
batch_sizeassignment:-self._batch_size = batch_size or self.settings.chroma_batch_size +self._batch_size = batch_size if batch_size is not None else self.settings.chroma_batch_sizeThis change makes the assignment more explicit and avoids potential issues with falsy values for
batch_size.Also applies to: 33-33, 36-36
59-66: OpenAI embedding support implemented.The addition of OpenAI embedding support is well-implemented, using the settings to determine whether to use OpenAI embeddings and instantiating the
OpenAIEmbeddingFunctionwith the appropriate parameters.Consider adding error handling for potential issues with OpenAI API:
if self.settings.use_openai_embeddings and self.settings.openai_api_key: - self._embedding_function = OpenAIEmbeddingFunction( - api_key=self.settings.openai_api_key, - model_name=self.settings.openai_embedding_model - ) + try: + self._embedding_function = OpenAIEmbeddingFunction( + api_key=self.settings.openai_api_key, + model_name=self.settings.openai_embedding_model + ) + except Exception as e: + logger.error(f"Failed to initialize OpenAI embedding function: {e}") + logger.info("Falling back to default embedding function") + self._embedding_function = DEFAULT_EMBEDDING_FUNCTION else: self._embedding_function = embedding_function or DEFAULT_EMBEDDING_FUNCTIONThis change adds robustness by gracefully handling potential issues with the OpenAI API initialization.
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126-265: Consider enhancing unchanged methods for consistency.While the changes made are good, consider applying similar enhancements to the unchanged methods:
- Add logging statements to methods like
delete_docs,get_relevant_docs, andget_relevant_segmentsfor consistent observability.- Review these methods to ensure they work correctly with the new OpenAI embedding functionality.
- Consider adding type hints to improve code readability and maintainability.
Example of adding logging to
delete_docs:def delete_docs( self, ids: Optional[List[str]] = None, where: Optional[dict] = None ) -> Optional[bool]: + logger.info(f"Deleting documents: ids={ids}, where={where}") if ids is None and where is not None: records_to_delete = self._docs_collection.get(where=where) ids = records_to_delete["ids"] self._docs_collection.delete(ids=ids) + logger.info(f"Deleted {len(ids)} documents") return TrueThese suggestions will help maintain consistency across the entire class and improve its overall quality.
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📒 Files selected for processing (9)
- backend/.env.example (1 hunks)
- backend/Makefile (1 hunks)
- backend/app/api/v1/projects.py (1 hunks)
- backend/app/config.py (1 hunks)
- backend/app/database/init.py (1 hunks)
- backend/app/logger.py (1 hunks)
- backend/app/vectorstore/chroma.py (3 hunks)
- backend/pyproject.toml (1 hunks)
- backend/tests/vectorstore/test_chroma_db.py (1 hunks)
🚧 Files skipped from review as they are similar to previous changes (7)
- backend/.env.example
- backend/Makefile
- backend/app/api/v1/projects.py
- backend/app/config.py
- backend/app/database/init.py
- backend/app/logger.py
- backend/tests/vectorstore/test_chroma_db.py
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🔇 Additional comments (4)
backend/pyproject.toml (1)
19-19: Addition of OpenAI dependency looks good.The addition of the
openaipackage as a dependency aligns with the PR objective of adding support for OpenAI embedding. The version constraint^1.51.2is appropriate, allowing for compatible updates within the 1.x range.A few points to consider:
- Ensure that this dependency is actually used in the codebase to avoid unnecessary bloat.
- Be aware that this addition might increase the project's attack surface, so make sure to follow security best practices when using the OpenAI API.
- Consider adding a comment explaining why this dependency was added, for future maintainers.
Let's verify the usage of this new dependency:
backend/app/vectorstore/chroma.py (3)
3-3: Improved configuration management and logging capabilities.The addition of
pydantic_settings, custom settings, and a logger enhances the robustness and observability of theChromaDBclass. These changes will facilitate better configuration management and debugging.Also applies to: 6-6, 8-8, 13-14
11-11: Support for OpenAI embedding added.The import of
OpenAIEmbeddingFunctionaligns with the PR objective of adding support for OpenAI embedding. This addition will allow for more advanced embedding capabilities in theChromaDBclass.
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1-265: Overall assessment: Well-implemented OpenAI embedding support with room for minor improvementsThe changes in this file successfully implement support for OpenAI embeddings, aligning well with the PR objective. The additions enhance the
ChromaDBclass's configurability, functionality, and observability. Key improvements include:
- Better configuration management using
pydantic_settingsand a custom settings object.- Implementation of OpenAI embedding support with appropriate conditional logic.
- Enhanced logging for better observability.
- Improved flexibility in the
add_docsmethod with the newbatch_sizeparameter.While the implementation is generally good, consider addressing the following points to further improve the code:
- Add error handling for OpenAI API initialization.
- Refactor the
add_docsmethod for better readability and maintainability.- Ensure proper handling of
Nonevalues forbatch_size.- Apply consistent logging and potential improvements to the unchanged methods.
Overall, this is a solid implementation that achieves the PR objective while introducing valuable enhancements to the
ChromaDBclass.
| @@ -63,33 +74,54 @@ def add_docs( | |||
| docs: Iterable[str], | |||
| ids: Optional[Iterable[str]] = None, | |||
| metadatas: Optional[List[dict]] = None, | |||
| batch_size=5, | |||
| batch_size: Optional[int] = None, | |||
There was a problem hiding this comment.
Improved document addition with OpenAI embedding support and better logging.
The changes to the add_docs method enhance its functionality by supporting OpenAI embeddings and improving logging. The addition of the batch_size parameter increases flexibility.
The current implementation doesn't handle the case where both batch_size and self._batch_size are None. Consider adding a default value:
-batch_size = batch_size or self._batch_size
+batch_size = batch_size or self._batch_size or 100 # Default to 100 if both are NoneTo improve readability and maintainability, consider extracting the document addition logic into separate methods:
def _add_docs_openai(self, docs, metadatas, ids):
logger.info("Using OpenAI embeddings")
self._docs_collection.add(
documents=list(docs),
metadatas=metadatas,
ids=ids,
)
def _add_docs_batched(self, docs, metadatas, ids, batch_size):
logger.info("Using default embedding function")
for i in range(0, len(docs), batch_size):
logger.info(f"Processing batch {i} to {i + batch_size}")
self._docs_collection.add(
documents=docs[i : i + batch_size],
metadatas=metadatas[i : i + batch_size],
ids=ids[i : i + batch_size],
)
# In the add_docs method:
if self.settings.use_openai_embeddings and self.settings.openai_api_key:
self._add_docs_openai(docs, metadatas, ids)
else:
self._add_docs_batched(docs, metadatas, ids, batch_size)This refactoring improves the readability and maintainability of the add_docs method.
Also applies to: 85-85, 101-124
| else: | ||
| _client_settings = config.Settings( | ||
| is_persistent=True, anonymized_telemetry=False | ||
| ) | ||
| _client_settings.persist_directory = settings.chromadb_url | ||
| _client_settings.persist_directory = self.settings.chromadb_url |
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💡 Codebase verification
Inconsistent persist_directory Assignments Detected
The following instances set persist_directory to values other than self.settings.chromadb_url:
backend/app/vectorstore/chroma.py: _client_settings.persist_directory = persist_pathbackend/app/vectorstore/chroma.py: self._persist_directory = _client_settings.persist_directory
Please ensure that all assignments to persist_directory use self.settings.chromadb_url to maintain configuration consistency.
🔗 Analysis chain
Persistence directory now set from settings.
The use of self.settings.chromadb_url for setting the persistence directory ensures consistency with the new configuration approach.
Verify that this change doesn't break existing implementations:
If the script returns results, consider updating those occurrences to use the new settings approach.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Check for uses of the old persistence directory setting
rg --type python "persist_directory\s*=\s*(?!self\.settings\.chromadb_url)"
Length of output: 361
Script:
#!/bin/bash
# Check for uses of the old persistence directory setting using PCRE2
rg --pcre2 --type python "persist_directory\s*=\s*(?!self\.settings\.chromadb_url)"
Length of output: 116
Script:
#!/bin/bash
# Check for uses of the old persistence directory setting using PCRE2 with correct file type
rg --pcre2 --type py "persist_directory\s*=\s*(?!self\.settings\.chromadb_url)"
Length of output: 474
* feat: add support for openai embeddings * fix: install openai
Summary by CodeRabbit
Release Notes
New Features
Bug Fixes
Tests
ChromaDBclass, validating various functionalities and configurations.