create/fix tests and bugs

[dayesouza]

This pull request introduces several improvements and bug fixes across the codebase, with a focus on enhancing unit test coverage, fixing minor bugs, and updating some API behaviors for clarity and correctness. The most significant changes include the addition of new unit tests for core AI components, refactoring for more accurate API responses, and minor corrections to constant definitions and logic.

Testing improvements:

• Added comprehensive unit tests for BaseBatchAsync, BaseChat, and AI-related classes (LLMCallback, VectorData) to ensure correct initialization, callback handling, and async behavior. These tests increase coverage and help catch regressions early. [1] [2] [3]
• Updated the changelog to reflect the addition of new unit tests for coverage.

Bug fixes and API improvements:

• Fixed a bug in add_df_to_model in match_entity_records/api.py by ensuring the dataframe name is correctly converted to a string, preventing type errors.
• Updated get_attributes and remove_attributes in detect_entity_networks/api.py to use the correct Polars DataFrame API (schema instead of columns, and to_list() instead of tolist()), ensuring compatibility and correctness.
• Refactored query rewriting logic in query_rewriter.py to more robustly filter out the "dummynode" from concepts, improving query accuracy.
• Renamed test_history_elements to parse_history_elements in query_text_data/helper_functions.py for clarity, and updated all usages in relevance_assessor.py to match. [1] [2] [3] [4] [5]

Constants and configuration:

• Added a new constant PDF_MARGIN_INCHES to helpers/constants.py for better PDF generation control.

Build and CI configuration:

• Removed legacy Docker build and artifact publishing steps from .vsts-ci.yml to streamline the CI pipeline.