Feat: destination vector db add option for embedding without field names #91
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Sequence Diagram(s)sequenceDiagram
participant User
participant Config
participant Processor
User->>Config: Set omit_field_names_from_embeddings to true
Config-->>Processor: Pass configuration
Processor->>Processor: Check omit_field_names_from_embeddings
alt If true
Processor->>Processor: Call _extract_values_from_dict
else If false
Processor->>Processor: Call stringify_dict
end
Processor-->>User: Return processed document
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (4)
airbyte_cdk/destinations/vector_db_based/document_processor.py (2)
166-169: Consider simplifying the boolean check and fixing indentation.The logic is correct, but we could make it more Pythonic. What do you think about these small improvements? 🤔
- if self.omit_field_names_from_embeddings == False: - text = stringify_dict(relevant_fields) - else: - text = self._extract_values_from_dict(relevant_fields) + if self.omit_field_names_from_embeddings: + text = self._extract_values_from_dict(relevant_fields) + else: + text = stringify_dict(relevant_fields)This suggestion:
- Uses a more idiomatic Python boolean check
- Puts the new feature case first (positive condition)
- Fixes indentation consistency
wdyt? 🙂
🧰 Tools
🪛 Ruff (0.8.0)
166-166: Avoid equality comparisons to
False; useif not self.omit_field_names_from_embeddings:for false checksReplace with
not self.omit_field_names_from_embeddings(E712)
173-180: Consider enhancing the helper method with type hints and edge cases.The recursive implementation looks clean! Would you be interested in adding some enhancements for better maintainability and robustness? 🛠️
- def _extract_values_from_dict(self, data): + def _extract_values_from_dict(self, data: Any) -> str: + if data is None: + return "" if isinstance(data, dict): - return "\n".join(self._extract_values_from_dict(value) for value in data.values()) + return "\n".join(filter(None, (self._extract_values_from_dict(value) for value in data.values()))) elif isinstance(data, list): - return "\n".join(self._extract_values_from_dict(item) for item in data) + return "\n".join(filter(None, (self._extract_values_from_dict(item) for item in data))) else: return str(data)This suggestion adds:
- Type hints for better code maintainability
- Explicit handling of None values
- Filtering of empty strings to avoid unnecessary newlines
How does this look to you? 🤔
airbyte_cdk/destinations/vector_db_based/config.py (1)
111-116: The implementation looks great! Would you consider some small enhancements? 🤔The new field is well-documented and has a sensible default. A couple of suggestions to make it even better:
- Maybe we could add an example to the description to make it super clear? Something like:
description="Do not include the field names in the text that gets embedded. By default field names are embedded e.g 'user: name, user.email: [email protected]'. If set to true, only the values are embedded e.g. 'name, [email protected]'.", + examples=[True, False],
- Since this field works in conjunction with
text_fields, should we add a note about their interaction? For instance, whentext_fieldsis empty (meaning all fields are considered), how doesomit_field_names_from_embeddingsaffect the behavior? wdyt?unit_tests/destinations/vector_db_based/config_test.py (1)
245-251: LGTM! The new configuration field looks well-structured 🎉The implementation aligns perfectly with the PR objectives. The field is properly integrated with appropriate metadata and maintains backward compatibility with the default value of
False.Hey @frankiebromage, what do you think about enhancing the description slightly to include an example of the embedding format? Something like:
- "description": "Do not include the field names in the text that gets embedded. By default field names are embedded e.g 'user: name, user.email: [email protected]'. If set to true, only the values are embedded e.g. 'name, [email protected]'.", + "description": "Do not include the field names in the text that gets embedded. By default field names are embedded (e.g., 'user.name: John Doe, user.email: [email protected]'). If set to true, only the values are embedded (e.g., 'John Doe, [email protected]').",The suggested description uses more realistic examples that better illustrate the difference. Wdyt? 🤔
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airbyte_cdk/destinations/vector_db_based/config.py(1 hunks)airbyte_cdk/destinations/vector_db_based/document_processor.py(2 hunks)unit_tests/destinations/vector_db_based/config_test.py(1 hunks)unit_tests/destinations/vector_db_based/document_processor_test.py(2 hunks)
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🧰 Additional context used
🪛 Ruff (0.8.0)
airbyte_cdk/destinations/vector_db_based/document_processor.py
166-166: Avoid equality comparisons to False; use if not self.omit_field_names_from_embeddings: for false checks
Replace with not self.omit_field_names_from_embeddings
(E712)
🔇 Additional comments (4)
airbyte_cdk/destinations/vector_db_based/document_processor.py (2)
128-128: LGTM! Clean integration of the new config option.
The new instance variable follows the established pattern of other configuration options in the class.
166-169: Verify impact on existing vector collections.
Just wanted to flag something important - enabling this feature on existing collections would create embeddings with a different structure (without field names) compared to previous ones. This might affect similarity search results.
Would you like help documenting this potential impact in the README or adding tests to verify the behavior? 🤝
🧰 Tools
🪛 Ruff (0.8.0)
166-166: Avoid equality comparisons to False; use if not self.omit_field_names_from_embeddings: for false checks
Replace with not self.omit_field_names_from_embeddings
(E712)
airbyte_cdk/destinations/vector_db_based/config.py (1)
111-116: Shall we verify the test coverage? 🧪
The change looks great! Since this affects how text is processed for embeddings, it would be good to verify that we have comprehensive test coverage. Could you confirm if:
- The DocumentProcessor tests include cases for both
TrueandFalsevalues of this field? - The integration tests (if any) verify that the embeddings are correctly generated with and without field names?
✅ Verification successful
Looking good! Test coverage is in place ✅
The code is well-tested with both True and False cases for omit_field_names_from_embeddings in document_processor_test.py. The implementation in document_processor.py correctly handles both cases, and the config schema is properly defined in config_test.py.
Great job on maintaining test coverage while adding this new feature! 🎯
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#!/bin/bash
# Check for test coverage of the new field
rg -l "omit_field_names_from_embeddings.*=.*(?:True|False)" "test_*.py"
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# Let's first find the test files in the correct directory
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rg -l "field.*names.*embed" --type py
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# Let's check the test coverage in document_processor_test.py
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# Also check config_test.py for any related tests
rg "omit_field_names_from_embeddings" unit_tests/destinations/vector_db_based/config_test.py -B 2 -A 2
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rg "omit_field_names_from_embeddings" airbyte_cdk/destinations/vector_db_based/document_processor.py -B 2 -A 2
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unit_tests/destinations/vector_db_based/config_test.py (1)
245-251: Verify test coverage for the new configuration option
While the schema test looks good, we might want to ensure we have proper test coverage for this functionality. Would you like me to help verify if we have test cases covering:
- The behavior when
omit_field_names_from_embeddingsis True - The behavior when
omit_field_names_from_embeddingsis False (default case)
✅ Verification successful
Test coverage looks good for the new configuration option! 🎉
I found test cases in document_processor_test.py that explicitly verify both scenarios:
- When
omit_field_names_from_embeddings = False(default case), confirming field names are included in the embedded text - When
omit_field_names_from_embeddings = True, verifying only values are embedded
The assertions check the exact expected output format in both cases, which is quite thorough. Nice work on the test coverage! 👍
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rg -A 5 "def test.*embed.*field.*name" "unit_tests"
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🧹 Outside diff range and nitpick comments (3)
airbyte_cdk/destinations/vector_db_based/document_processor.py (3)
128-128: Consider adding documentation for the new parameterWould you consider adding a docstring explanation for
omit_field_names_from_embeddingsto help users understand its purpose and impact? Something like:# Controls whether field names should be included when generating embeddings. # When True, only field values are used, potentially improving semantic similarity.wdyt? 🤔
166-169: Consider extracting text generation logicThe conditional text generation logic is clean, but what do you think about extracting it into a dedicated method for better readability? Something like:
- if not self.omit_field_names_from_embeddings: - text = stringify_dict(relevant_fields) - else: - text = self._extract_values_from_dict(relevant_fields) + text = self._generate_text_from_fields(relevant_fields)With a new method:
def _generate_text_from_fields(self, fields: Dict[str, Any]) -> str: return stringify_dict(fields) if not self.omit_field_names_from_embeddings else self._extract_values_from_dict(fields)This would make the main method more focused and easier to read. What are your thoughts? 🤔
173-180: Great implementation! Here are some suggestions to make it even betterThe recursive implementation looks solid! Would you consider these enhancements?
- Add return type hint:
- def _extract_values_from_dict(self, data: Union[dict, list, Any]) -> str: + def _extract_values_from_dict(self, data: Union[dict, list, Any]) -> str:
- Add docstring explaining the recursion strategy:
""" Recursively extracts values from nested data structures. Handles dictionaries, lists, and scalar values, joining them with newlines. """
- For large nested structures, consider adding a join_char parameter with a default value of '\n':
def _extract_values_from_dict(self, data: Union[dict, list, Any], join_char: str = '\n') -> str: if isinstance(data, dict): return join_char.join(self._extract_values_from_dict(value, join_char) for value in data.values()) elif isinstance(data, list): return join_char.join(self._extract_values_from_dict(item, join_char) for item in data) else: return str(data)This would make the method more flexible for different use cases. What do you think? 🤔
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- unit_tests/destinations/vector_db_based/document_processor_test.py: Error: Server error: no LLM provider could handle the message
🔇 Additional comments (2)
airbyte_cdk/destinations/vector_db_based/document_processor.py (2)
8-8: LGTM! Clean import addition.
The addition of Union to the typing imports is appropriate for the new method's type hints.
166-180: Verify test coverage for the new functionality
The changes look good! Would you mind verifying that we have test coverage for:
- Different combinations of nested structures with
omit_field_names_from_embeddings - Edge cases like empty dicts/lists
- Performance with large nested structures
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🧹 Outside diff range and nitpick comments (4)
airbyte_cdk/destinations/vector_db_based/document_processor.py (3)
128-128: Consider updating the docstring to reflect the new behavior?The _generate_document method now supports optional field name omission, but this isn't documented. Would you consider adding this to the docstring? Something like:
def _generate_document(self, record: AirbyteRecordMessage) -> Optional[Document]: + """ + Generate a Document from an AirbyteRecordMessage. + + The text content can be generated either with or without field names based on + the omit_field_names_from_embeddings configuration. + """wdyt? 🤔
Also applies to: 166-168
170-174: The implementation looks clean! Quick suggestion about type hints.The method implementation is clear and follows the single responsibility principle. Would you consider adding a return type hint to make it even more explicit? Like this:
- def _generate_text_from_fields(self, fields: Dict[str, Any]) -> str: + def _generate_text_from_fields(self, fields: Dict[str, Any]) -> str:
176-184: The implementation looks solid! A few enhancement ideas to consider.The recursive implementation is clean and handles different data types well. Would you consider these enhancements?
- Adding a docstring to explain the recursive nature and the join_char parameter:
def _extract_values_from_dict(self, data: Union[Dict[Any, Any], List[Any], Any], join_char: str = '\n') -> str: """ Recursively extracts values from nested data structures, joining them with the specified character. Args: data: The data structure to process (dict, list, or scalar value) join_char: The character to use when joining extracted values (default: newline) Returns: A string containing all values joined by join_char """
- Optimizing empty collection handling:
elif isinstance(data, dict): + if not data: + return "" return join_char.join(self._extract_values_from_dict(value) for value in data.values()) elif isinstance(data, list): + if not data: + return "" return join_char.join(self._extract_values_from_dict(item) for item in data)wdyt about these suggestions? 🤔
airbyte_cdk/destinations/vector_db_based/config.py (1)
111-116: LGTM! Clean implementation of the field names toggle 🎯The implementation looks solid with good documentation and sensible defaults. Just wondering if we could make the description even more helpful by adding an example of when users might want to enable this option? For instance, when doing semantic similarity searches where field names could skew the results? wdyt?
description="Do not include the field names in the text that gets embedded. By default field names are embedded (e.g., 'user.name: John Doe \n user.email: [email protected]'). If set to true, only the values are embedded (e.g., 'John Doe \n [email protected]').",Could be enhanced to:
description="Do not include the field names in the text that gets embedded. By default field names are embedded (e.g., 'user.name: John Doe \n user.email: [email protected]'). If set to true, only the values are embedded (e.g., 'John Doe \n [email protected]'). This is particularly useful when doing semantic similarity searches where field names could skew the vector representation.",
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airbyte_cdk/destinations/vector_db_based/config.py(1 hunks)airbyte_cdk/destinations/vector_db_based/document_processor.py(3 hunks)unit_tests/destinations/vector_db_based/config_test.py(1 hunks)unit_tests/destinations/vector_db_based/document_processor_test.py(2 hunks)
🔥 Files not summarized due to errors (1)
- unit_tests/destinations/vector_db_based/document_processor_test.py: Error: Server error: no LLM provider could handle the message
🚧 Files skipped from review as they are similar to previous changes (1)
- unit_tests/destinations/vector_db_based/config_test.py
🔇 Additional comments (3)
airbyte_cdk/destinations/vector_db_based/document_processor.py (2)
8-8: LGTM! Clean import addition.
The Union type import is appropriately added to support the new type hints.
Line range hint 128-184: Verify test coverage for the new embedding option
The changes look well-implemented! Let's verify that we have adequate test coverage for the new functionality.
✅ Verification successful
Test coverage looks good for the new embedding option! 🎉
The new omit_field_names_from_embeddings feature is well-tested:
- It's covered in the document processor tests with both
TrueandFalsecases - The config schema is properly defined with a clear description and default value
- The implementation is properly validated through test assertions
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echo "Searching for related test files..."
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omit_field_names_from_embeddings
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rg "omit_field_names_from_embeddings" -A 5 -B 5 unit_tests/destinations/vector_db_based/document_processor_test.py
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airbyte_cdk/destinations/vector_db_based/config.py (1)
111-116: Verify test coverage for the new configuration option
Let's make sure we have good test coverage for this new field.
✅ Verification successful
Test coverage looks good for the new configuration option
I found that the omit_field_names_from_embeddings field is well tested in document_processor_test.py with both True and False cases, verifying the expected behavior for both including and excluding field names from embeddings. The implementation in DocumentProcessor class correctly uses this configuration to control the text formatting.
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# Description: Check for test coverage of the new field
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fd "test.*\.py" | while read -r file; do
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🧹 Outside diff range and nitpick comments (2)
airbyte_cdk/destinations/vector_db_based/document_processor.py (2)
170-175: Consider adding a docstring for the new method?The method looks good, but adding a docstring would help explain its purpose and parameters, wdyt? Something like:
def _generate_text_from_fields(self, fields: Dict[str, Any]) -> str: """ Generate text from fields with optional field name omission. Args: fields: Dictionary of field names and their values Returns: A string representation of the fields, with or without field names based on omit_field_names_from_embeddings configuration """
176-187: The implementation looks solid! A few suggestions to consider:
- Would you consider adding a docstring to explain the recursive nature and join_char parameter?
- For large nested structures, we might want to consider using a generator to optimize memory usage, wdyt?
Here's a potential enhancement:
def _extract_values_from_dict( self, data: Union[Dict[Any, Any], List[Any], Any], join_char: str = "\n" ) -> str: """ Recursively extracts values from nested structures without field names. Args: data: The data structure to extract values from join_char: The character used to join multiple values Returns: A string containing all values joined by join_char """ def _generate_values(): if data is None: yield "" elif isinstance(data, dict): for value in data.values(): yield from (v for v in _generate_values(value)) elif isinstance(data, list): for item in data: yield from (v for v in _generate_values(item)) else: yield str(data) return join_char.join(_generate_values())
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airbyte_cdk/destinations/vector_db_based/document_processor.py(3 hunks)
🔇 Additional comments (3)
airbyte_cdk/destinations/vector_db_based/document_processor.py (3)
8-8: LGTM! Clean import addition.
The Union type import is properly organized with other typing imports and is necessary for the new method parameter typing.
128-128: LGTM! Clean configuration addition.
The new configuration parameter is properly initialized following the existing pattern.
170-187: Verify test coverage for the new functionality
The implementation looks great and meets the PR objectives! Let's verify the test coverage:
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🧹 Outside diff range and nitpick comments (2)
airbyte_cdk/destinations/vector_db_based/document_processor.py (2)
171-175: Add docstring for clarity?The method looks clean and follows good practices. Would you like to add a docstring explaining the purpose and behavior of this method? Something like:
def _generate_text_from_fields(self, fields: Dict[str, Any]) -> str: + """ + Generates text from fields either with or without field names based on configuration. + + Args: + fields: Dictionary of field names and their values + Returns: + A string representation of the fields + """ if self.omit_field_names_from_embeddings:wdyt? 🤔
177-187: Consider adding a depth limit?The recursive implementation looks solid! However, for safety against potential stack overflow with deeply nested structures, maybe we could add a depth limit? Here's a thought:
def _extract_values_from_dict( - self, data: Union[Dict[Any, Any], List[Any], Any], join_char: str = "\n" + self, data: Union[Dict[Any, Any], List[Any], Any], join_char: str = "\n", depth: int = 100 ) -> str: if data is None: return "" + if depth <= 0: + return str(data) elif isinstance(data, dict): - return join_char.join(self._extract_values_from_dict(value) for value in data.values()) + return join_char.join(self._extract_values_from_dict(value, join_char, depth - 1) for value in data.values())This would prevent potential issues with circular references or extremely deep structures. wdyt? 🤔
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airbyte_cdk/destinations/vector_db_based/config.py(1 hunks)airbyte_cdk/destinations/vector_db_based/document_processor.py(3 hunks)unit_tests/destinations/vector_db_based/config_test.py(1 hunks)unit_tests/destinations/vector_db_based/document_processor_test.py(2 hunks)
🔥 Files not summarized due to errors (1)
- unit_tests/destinations/vector_db_based/document_processor_test.py: Error: Server error: no LLM provider could handle the message
🚧 Files skipped from review as they are similar to previous changes (2)
- unit_tests/destinations/vector_db_based/config_test.py
- airbyte_cdk/destinations/vector_db_based/config.py
🔇 Additional comments (3)
airbyte_cdk/destinations/vector_db_based/document_processor.py (3)
8-8: LGTM! Clean import addition
The Union type import is well-placed with other typing imports and properly supports the new type hints.
171-187: Verify test coverage
The new functionality looks solid! Shall we verify that it's properly covered by tests?
129-129: LGTM! Let's verify the config integration
The new config parameter is properly integrated. Would you like me to verify that the ProcessingConfigModel properly defines this field?
✅ Verification successful
Config integration looks perfect! 🎯
The omit_field_names_from_embeddings field is properly defined in ProcessingConfigModel with:
- Correct type (bool)
- Sensible default (False)
- Clear title and descriptive help text
- Follows the same pattern as other config parameters
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#!/bin/bash
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rg -U "omit_field_names_from_embeddings.*Field" airbyte_cdk/destinations/vector_db_based/config.py
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#!/bin/bash
# Let's get more context around the field definition
rg -A 3 -B 3 "omit_field_names_from_embeddings" airbyte_cdk/destinations/vector_db_based/config.py
Length of output: 581
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@frankiebromage - Thanks for creating this! Just a heads up: one challenge with rolling this out will be that most of the vector store destinations are several versions behind in their CDK pin. This may be related to the reason the Pinecone test failed in CI. |
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I added both of these issues to track discussions around bumping one of either Pinecone or Milvus.
Getting one or both of these to the latest CDK version would give us more confidence in changing CDK functionality around vector destinations. I've also updated (for now at least) the default connector to test here in this PR - setting it to PGVector, since that is the most recent vector-based connector that we have updated. |
Description
Responding to issue #48627
Including field names when embedding text changes the semantic meaning of a vector, so it is useful to have the option of embedding only the text without the field names.
This is my first contribution, so please let me know what else is needed!
Changes
Adds an additional config to allow user to choose whether or not to embed field names with a vector db destination.
Summary by CodeRabbit
New Features
Bug Fixes
Tests
omit_field_names_from_embeddingsfield in the configuration model to ensure proper functionality.Resolves airbytehq/airbyte#48627 (comment)