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POST /vectors/hybrid takes a question + SQL WHERE clause. Pipeline: 1. SQL filter narrows to structurally-valid candidates (role, state, reliability, certs — whatever the caller specifies) 2. Brute-force cosine scores ALL embeddings (not HNSW, which caps at ~30 results due to ef_search — too few to intersect with narrow SQL filters on 10K+ datasets) 3. Filter vector results to only SQL-verified IDs 4. LLM generates answer from verified-correct records Tested on the exact query that failed the staffing simulation: "forklift operators in IL with reliability > 0.8" — SQL found 78 matches, vector ranked the 5 most semantically relevant, LLM generated an answer citing real workers with actual skills and certifications. Every source marked sql_verified=true. This closes the architectural gap identified by the quality eval: structured precision (SQL) + semantic intelligence (vector) in one endpoint. The simulation's contract-matching path was already SQL-pure and worked perfectly; now the intelligence-question path has the same accuracy. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
27 lines
818 B
TOML
27 lines
818 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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catalogd = { path = "../catalogd" }
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queryd = { path = "../queryd" }
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# ADR-019 firewall — vectord-lance owns its own Arrow 57 / Lance 4 deps.
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# Public API uses only std types so no version conflict propagates here.
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vectord-lance = { path = "../vectord-lance" }
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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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uuid = { workspace = true }
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