ES2-979: score-returning similarity search

[inkme9]

• add tuple-returning similarity search API
• add similarity_search_with_score / _by_vector wrappers
• dedupe code through shared helper and keep metadata intact
• extend unit/integration coverage

[Copilot]

Pull Request Overview

This PR adds score-returning similarity search functionality to the Envector vector store, enhancing the API with tuple-based methods that return both documents and their similarity scores.

• Introduces similarity_search_with_score and similarity_search_with_score_by_vector methods
• Refactors similarity search to use a shared internal helper method _similarity_search_with_scores
• Extends test coverage with comprehensive unit and integration tests for the new functionality

Reviewed Changes

Copilot reviewed 20 out of 21 changed files in this pull request and generated 2 comments.

File	Description
libs/envector/langchain_envector/vectorstore.py	Core implementation of score-returning similarity search methods and refactored search logic
tests/test_vectorstore.py	Comprehensive unit tests for new score-returning methods and existing functionality
tests/integration/test_es2_integration.py	Code formatting improvements for better readability
Multiple other files	Code formatting, import cleanup, and removal of unused imports

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[euphoria0-0]

테스트 완료

store.similarity_search(query, k=3)
> [Document(metadata={'source': 'paper.pdf', 'page': 1, 'chunk': 0, '_score': 0.5060499906539917, '_id': 3}, page_content='chunk-1'), ...]

store.similarity_search_with_score(query, k=3)
> [(Document(metadata={'source': 'paper.pdf', 'page': 1, 'chunk': 0, '_score': 0.5060520768165588, '_id': 1}, page_content='chunk-1'), 0.5060520768165588), ...]

store.similarity_search_by_vector(query_embedding, k=3)
> [Document(metadata={'source': 'paper.pdf', 'page': 1, 'chunk': 0, '_score': 0.5060375332832336, '_id': 1}, page_content='chunk-1'), ...)]

[euphoria0-0]

@inkme9 테스트 완료했습니다! 머지해도 될까요?