fix: update anchor_bfs_partitioner & fix vqa_generator

[ChenZiHong-Gavin]

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[gemini-code-assist]

Summary of Changes

Hello @ChenZiHong-Gavin, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request focuses on refining two distinct components: the VQA generator and the anchor BFS partitioner. The VQA generator's output format has been adjusted to align with a more structured representation for answers, potentially enhancing compatibility or future extensibility. Concurrently, the anchor BFS partitioner has undergone a significant refactoring to improve its efficiency and clarity by directly leveraging the graph storage interface for neighbor lookups, thereby removing an intermediate adjacency list representation.

Highlights

• VQA Generator Output Format Update: The vqa_generator.py file has been updated to change the format of the generated answers. The value or content field for GPT/assistant responses now expects a list containing a dictionary with a 'text' key, rather than a direct string.
• Anchor BFS Partitioner Refactoring: The anchor_bfs_partitioner.py has been refactored to streamline graph traversal. It now directly utilizes the BaseGraphStorage object (g) to retrieve neighbors, eliminating the need to pre-build and pass an explicit adjacency list (adj) to the _grow_community method.
• Removed Redundant Adjacency List: The partition method in anchor_bfs_partitioner.py no longer constructs an adjacency list from all edges, simplifying the initial setup for community partitioning.

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[gemini-code-assist]

Code Review

This pull request introduces two main improvements. First, it refactors the AnchorBFSPartitioner to use the BaseGraphStorage interface directly for fetching graph neighbors, instead of pre-building an adjacency list. This simplifies the code, improves encapsulation, and likely enhances performance and memory efficiency for large graphs. Second, it fixes the output format in VQAGenerator for 'Sharegpt' and 'ChatML' to correctly structure the response for multi-modal content, ensuring consistency with the input format. The changes are well-implemented and improve the overall quality of the codebase.