feat(logosdb): add LogosDB vector database integration

[jose-compu]

Summary

• Adds LogosDB as a supported vector database backend — a fast, embedded HNSW vector store written in C/C++ with Python bindings, backed by memory-mapped binary storage and hnswlib.
• Implements the full VectorDB interface: __init__, init context manager, insert_embeddings (via put_batch), search_embedding, and optimize.
• Registers DB.LogosDB in the enum and wires up init_cls, config_cls, and case_config_cls.
• Adds the logosdb CLI subcommand with a --uri flag (local directory path).
• Adds logosdb as an optional extra in pyproject.toml.

Design notes

• LogosDB is an embedded (single-process, file-based) database — no server required. The DB directory is passed via --uri.
• Distance metric is derived from the case MetricType at runtime (COSINE / L2 / IP). COSINE is the default and auto-normalizes vectors.
• Benchmark metadata IDs are stored as the text field (str(id)) and parsed back on search, since LogosDB's internal row IDs are independent of the benchmark ID space.
• HNSW index is built incrementally on insert; optimize() is a no-op with a log message.

Benchmark result

Tested on Performance1536D50K (OpenAI embeddings, 50K vectors, 1536 dim, COSINE) on Apple M-series:

Metric	Value
Load duration	340 s
Serial latency p99	4.6 ms
Serial latency p95	4.0 ms
Recall@100	0.9347
NDCG	0.9464

Test plan

• pip install logosdb (binary wheels for Linux x86_64/aarch64 and macOS x86_64/arm64, CPython 3.9-3.13)
• vectordbbench logosdb --uri /tmp/vdbbench_logosdb --case-type Performance1536D50K --skip-search-concurrent
• Verify recall, latency, and result JSON written to vectordb_bench/results/LogosDB/

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[jose-compu]

can you please review @sre-ci-robot @jkatz @javiervegas @claude ?