fix(metrics): fixed NDCG calculation and added comprehensive test cases

[iamarunbrahma]

Description

This PR fixes a bug in the NDCG (Normalized Discounted Cumulative Gain) metric calculation where the IDCG was incorrectly calculated using retrieved_ids instead of expected_ids. The fix ensures IDCG represents the best possible ranking of relevant documents.

Key changes:

• Fixed IDCG calculation to use min(len(retrieved_ids), len(expected_ids))
• Added proper handling for empty expected_ids case
• Added comprehensive test cases covering various scenarios including:
  • Perfect ranking
  • Partial matches
  • No relevant docs
  • More relevant docs than retrieved
  • Single relevant doc
  • Different modes (linear/exponential)
  • Empty expected_ids

Fixes #17070

New Package?

Did I fill in the tool.llamahub section in the pyproject.toml and provide a detailed README.md for my new integration or package?

• Yes
• No

Version Bump?

Did I bump the version in the pyproject.toml file of the package I am updating? (Except for the llama-index-core package)

• Yes
• No

Type of Change

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• This change requires a documentation update

How Has This Been Tested?

• I added new unit tests to cover this change
  • Added comprehensive test cases in test_metrics.py covering various NDCG scenarios
  • Tests include edge cases like empty expected_ids and different ranking modes