Add full-text search benchmark support

[jamesgao-jpg]

Context

VDBBench did not have a dedicated native full-text search benchmark path. This PR adds FTS as a first-class benchmark workload so BM25-based text search can be evaluated through the same task, runner, and result pipeline used by the rest of VDBBench.

Summary

• Add full-text search benchmark support centered on BM25 text retrieval.
• Introduce FTS benchmark cases that load text documents, run text queries, and report comparable performance results.
• Wire FTS through backend execution and Streamlit task generation so cases can be launched from the existing benchmark interfaces.

Backends Covered

• Milvus: native BM25 full-text indexing/search configuration and execution path.
• ElasticCloud / Elasticsearch: BM25 text indexing/search path using Elasticsearch defaults.
• Vespa: text schema/query path for BM25-style FTS benchmarking.
• Turbopuffer: namespace-based full-text benchmark path.

Testing Infra Touched

• Dataset layer: add FTS dataset definitions and ir_datasets-based document/query/ground-truth loading.
• Case layer: add FTS performance case definitions and task assembly support.
• Runner layer: support FTS document loading plus serial and concurrent text-query search execution.
• Frontend layer: expose FTS cases and generate backend-specific FTS task configs from Streamlit.
• Result layer: format FTS benchmark outputs alongside existing VDBBench results.

Datasets Supported

• MS MARCO: small 100K, medium 1M, large 8.8M documents.
• HotpotQA: small 100K, medium 1M, large 5.2M documents.

Metrics

• Search metric type: BM25.
• Accuracy metric: recall@k against dataset relevance labels.
• Performance metrics: serial latency p95/p99, concurrent QPS, load duration, and optimize duration.

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