refactor: Implement unified serialization function #6044
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…d a unified serialize method
…ums, and generic types
… errors gracefully
…arious data types
…unction for consistency and maintainability
…lize method for improved clarity and maintainability
…unction for improved consistency and clarity
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… streamlining type checks
…nse for streamlined handling
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…tems_length for improved handling of outputs, logs, messages, and artifacts
…alization module for better organization
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…factor-serialization`) Here is the refactored and optimized Python program. ### Changes and Optimizations. 1. **Logging Type Identification (`get_type` function)**. - Simplified `get_type` function using `isinstance`. 2. **Message Retrieval (`get_message` function)**. - Simplified the `get_message` function using straightforward if-elif clauses. 3. **Building Output Logs (`build_output_logs` function)**. - Refactored loops to avoid redundant checks and reduced operations. 4. **Serialization Function**. - Removed redundant checks and used direct falls back for common serialization patterns. These changes streamline the code, minimize redundant operations, and make the code more readable while maintaining functionality and improving performance.
…ified serialize function
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…actor-serialization`) Certainly! Here's a more efficient version of the given program. The primary optimization performed here is removing the redundant `.apply()` call and directly truncating values in a more performant way. ### Changes Made. 1. **Removed redundant `apply` calls**: In the original code, there were nested `apply` calls which can be very slow on larger DataFrames. The new implementation converts the DataFrame to a list of dictionaries first and then truncates the values if needed. 2. **Optimized truncation logic**: Applied truncation directly while iterating over the dictionary after conversion from a DataFrame. This reduces overhead and improves readability. These changes should enhance the runtime performance of the serialization process, especially for larger DataFrames.
⚡️ Codeflash found optimizations for this PR📄 123% (1.23x) speedup for
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…or-serialization`) Certainly! Here is a more optimized version of the program. Changes made. 1. Replaced the `apply` method with dictionary comprehension. This avoids creating an intermediate Series, which can be an expensive operation. 2. Moved `_truncate_value` outside of the main function to keep the main function concise and focused.
⚡️ Codeflash found optimizations for this PR📄 234% (2.34x) speedup for
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…or-serialization`) Certainly! Here is a more optimized version of the program. Changes made. 1. Replaced the `apply` method with dictionary comprehension. This avoids creating an intermediate Series, which can be an expensive operation. 2. Moved `_truncate_value` outside of the main function to keep the main function concise and focused.
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…`refactor-serialization`) (#6079) * feat: Implement serialization functions for various data types and add a unified serialize method * optmize conditional Co-authored-by: codeflash-ai[bot] <148906541+codeflash-ai[bot]@users.noreply.github.com> * fix: Update string and list truncation to include ellipsis for clarity * ⚡️ Speed up function `_serialize_series` by 234% in PR #6044 (`refactor-serialization`) Certainly! Here is a more optimized version of the program. Changes made. 1. Replaced the `apply` method with dictionary comprehension. This avoids creating an intermediate Series, which can be an expensive operation. 2. Moved `_truncate_value` outside of the main function to keep the main function concise and focused. * refactor: Remove unused `_truncate_value` function from serialization module --------- Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@langflow.org> Co-authored-by: codeflash-ai[bot] <148906541+codeflash-ai[bot]@users.noreply.github.com>
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This PR is now faster! 🚀 @ogabrielluiz accepted my optimizations from: |
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…(`refactor-serialization`) (#6078) * feat: Implement serialization functions for various data types and add a unified serialize method * feat: Enhance serialization by adding support for primitive types, enums, and generic types * fix: Update Pinecone integration to use VectorStore and handle import errors gracefully * test: Add hypothesis-based tests for serialization functions across various data types * refactor: Replace custom serialization logic with unified serialize function for consistency and maintainability * refactor: Replace recursive serialization function with unified serialize method for improved clarity and maintainability * refactor: Replace custom serialization logic with unified serialize function for improved consistency and clarity * refactor: Enhance serialization logic by adding instance handling and streamlining type checks * refactor: Remove custom dictionary serialization from ResultDataResponse for streamlined handling * refactor: Enhance serialization in ResultDataResponse by adding max_items_length for improved handling of outputs, logs, messages, and artifacts * refactor: Move MAX_ITEMS_LENGTH and MAX_TEXT_LENGTH constants to serialization module for better organization * refactor: Simplify message serialization in Log model by utilizing unified serialize function * refactor: Remove unnecessary pytest marker from TestSerializationHypothesis class * optimize _serialize_bytes Co-authored-by: codeflash-ai[bot] <148906541+codeflash-ai[bot]@users.noreply.github.com> * feat: Add support for numpy integer type serialization * feat: Enhance serialization with support for pandas and numpy types * test: Add comprehensive serialization tests for numpy and pandas types * fix: Update _serialize_dispatcher to return string representation for unsupported types * fix: Update _serialize_dispatcher to return the object directly instead of its string representation * optmize conditional Co-authored-by: codeflash-ai[bot] <148906541+codeflash-ai[bot]@users.noreply.github.com> * optimize length check Co-authored-by: codeflash-ai[bot] <148906541+codeflash-ai[bot]@users.noreply.github.com> * fix: Update string and list truncation to include ellipsis for clarity * ⚡️ Speed up function `_serialize_dataframe` by 123% in PR #6044 (`refactor-serialization`) Certainly! Here's a more efficient version of the given program. The primary optimization performed here is removing the redundant `.apply()` call and directly truncating values in a more performant way. ### Changes Made. 1. **Removed redundant `apply` calls**: In the original code, there were nested `apply` calls which can be very slow on larger DataFrames. The new implementation converts the DataFrame to a list of dictionaries first and then truncates the values if needed. 2. **Optimized truncation logic**: Applied truncation directly while iterating over the dictionary after conversion from a DataFrame. This reduces overhead and improves readability. These changes should enhance the runtime performance of the serialization process, especially for larger DataFrames. --------- Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@langflow.org> Co-authored-by: codeflash-ai[bot] <148906541+codeflash-ai[bot]@users.noreply.github.com>
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This PR is now faster! 🚀 @ogabrielluiz accepted my optimizations from: |
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Introduce a unified serialization method for various data types, improving consistency and maintainability. Enhance Pinecone integration to utilize VectorStore and handle import errors gracefully. Add comprehensive tests for the new serialization functions.