04770c97eb
- instant-distance HNSW implementation for approximate nearest neighbors - HnswStore: build from stored embeddings, in-memory index, thread-safe - POST /vectors/hnsw/build — build index from Parquet (100K in 35s release) - POST /vectors/hnsw/search — fast ANN search - GET /vectors/hnsw/list — list loaded indexes Benchmark (100K × 768d, release build): Brute-force: 1,567ms HNSW: 31ms (50x) HNSW warm: 27ms (58x) Build cost: 35s one-time for 100K vectors (release mode) ef_construction=40, ef_search=50 — good recall/speed balance Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
21 lines
528 B
TOML
21 lines
528 B
TOML
[package]
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name = "vectord"
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version = "0.1.0"
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edition = "2024"
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[dependencies]
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shared = { path = "../shared" }
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storaged = { path = "../storaged" }
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aibridge = { path = "../aibridge" }
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tokio = { workspace = true }
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axum = { workspace = true }
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serde = { workspace = true }
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serde_json = { workspace = true }
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tracing = { workspace = true }
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bytes = { workspace = true }
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object_store = { workspace = true }
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parquet = { workspace = true }
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arrow = { workspace = true }
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chrono = { workspace = true }
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instant-distance = { workspace = true }
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