Kdbai v1.4

[alexgiannak]

Update KDB.AI Integration and CLI Enhancements

• Purpose: Improve KDB.AI client integration and enhance command-line interface for exporting and importing data.
• Key Changes:
  • Updated kdbai-client dependency to version 1.4.0.
  • Added new command-line arguments for specifying KDB.AI endpoint and API key.
  • Refactored data export and import methods to use a database session instead of a direct endpoint.
  • Enhanced error handling and user prompts for missing parameters.
  • Updated Jupyter notebook to reflect changes in CLI commands and data structure.
• Impact: These changes streamline the user experience and improve the robustness of KDB.AI data operations.

✨ Generated with love by Kaizen ❤️

Original Description

# Update KDB.AI Client and Improve Import/Export Functionality

• **Purpose: ** Update the KDB.AI client library and enhance the import/export functionality for the VDF (Vector Data Format) IO module.
• Key Changes:
  • Upgraded the kdbai-client dependency to version 1.4.0 or higher.
  • Improved the ExportKDBAI and ImportKDBAI classes to handle various data types and provide a more robust import/export experience.
  • Added support for additional index types (QFLAT, QHNSW) in the ImportKDBAI class.
  • Streamlined the argument handling and user input prompts in both the export and import modules.
  • Optimized the data insertion process in the ImportKDBAI class to handle larger datasets more efficiently.
• **Impact: ** These changes will improve the reliability and usability of the VDF IO module when interacting with KDB.AI cloud or server instances. Users can now export and import a wider range of data types and index configurations, leading to a more seamless integration with KDB.AI.

✨ Generated with love by Kaizen ❤️

Original Description

# Update KDB.AI Client and Improve Import/Export Workflows

• ****Purpose: ** ** Update the KDB.AI client library, simplify the import/export workflows, and enhance the overall functionality.
• Key Changes:
  • Upgraded the KDB.AI client library to the latest version (>=1.4.0).
  • Simplified the argument parsing for KDB.AI endpoint and API key, allowing environment variables or user input.
  • Improved the table schema handling during import, supporting a wider range of data types.
  • Removed the max_num_rows limit and batch size handling, allowing full data import.
  • Streamlined the import and export processes, reducing complexity and improving reliability.
• ****Impact: ** ** The changes improve the overall user experience and reliability of the KDB.AI integration, making it easier to import and export data to/from the KDB.AI platform.

✨ Generated with love by Kaizen ❤️

Original Description

# Update KDB.AI Integration

• ******Purpose: ** ** ** Enhance the KDB.AI integration by updating dependencies and improving argument handling.
• Key Changes:
  • Updated kdbai-client dependency to version >=1.4.0.
  • Added new command-line arguments for kdbai_endpoint, kdbai_api_key, and tables_names in kdbai_export.py.
  • Refactored argument handling to check for None values and prompt for input if necessary.
  • Improved table schema definition and data insertion logic in kdbai_import.py.
  • Enhanced Jupyter notebook documentation for clarity on connecting to KDB.AI.
• ******Impact: ** ** ** These changes streamline the integration process and improve usability for developers working with KDB.AI.

✨ Generated with love by Kaizen ❤️

Original Description

Update to work with KDB.AI v1.4 that is coming out on Monday 21st

[dhruv-anand-aintech]

Thanks for sending this out, @alexgiannak! Let me have a look in a day or so

[kaizen-bot[bot]]

🔍 Code Review Summary

❗ Attention Required: This push has potential issues. 🚨

Overview

• Total Feedbacks: 2 (Critical: 2, Refinements: 0)
• Files Affected: 2
• Code Quality: [██████████████████░░] 90% (Excellent)

🚨 Critical Issues

best_practices (2 issues)

1. Use environment variables for sensitive information

---

📁 File: src/vdf_io/notebooks/kdbai_end_to_end_vectorIO.ipynb 🔍 Reasoning: Storing sensitive information like API keys directly in the code is a security risk. Using environment variables is a better practice to keep this information secure.

💡 Solution: Use environment variables to store the KDB.AI endpoint and API key, and retrieve them in the code. This way, the sensitive information is not exposed in the codebase.

Current Code:

['KDBAI_ENDPOINT = (', '    os.environ["KDBAI_ENDPOINT"]', '    if "KDBAI_ENDPOINT" in os.environ', '    else input("KDB.AI endpoint: ")', ')', 'KDBAI_API_KEY = (', '    os.environ["KDBAI_API_KEY"]', '    if "KDBAI_API_KEY" in os.environ', '    else getpass("KDB.AI API key: ")', ')']

Suggested Code:

['KDBAI_ENDPOINT = os.environ.get("KDBAI_ENDPOINT", input("KDB.AI endpoint: "))', 'KDBAI_API_KEY = os.environ.get("KDBAI_API_KEY", getpass("KDB.AI API key: "))']

2. Validate input for table name

---

📁 File: src/vdf_io/import_vdf/kdbai_import.py 🔍 Reasoning: Allowing users to provide arbitrary table names could lead to potential security vulnerabilities, such as SQL injection attacks.

💡 Solution: Implement input validation for the table name to ensure it follows a specific pattern or set of allowed characters. This will help prevent potential security issues.

Current Code:

['new_index_name = self.compliant_name(index_name)']

Suggested Code:

['import re', '', 'def validate_table_name(name):', "    allowed_pattern = r'^[a-zA-Z0-9_]+$'", '    if not re.match(allowed_pattern, name):', "        raise ValueError(f'Invalid table name:{name}. Only alphanumeric characters and underscores are allowed.')", '    return name', '', 'new_index_name = validate_table_name(self.compliant_name(index_name))']

---

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