26fc98c885
- vectord crate: chunk → embed → store → search → RAG - chunker: configurable chunk size + overlap, sentence-boundary aware splitting - store: embeddings as Parquet (binary blob f32 vectors), portable format - search: brute-force cosine similarity (works up to ~100K vectors) - rag: full pipeline — embed question → search index → retrieve context → LLM answer - Endpoints: POST /vectors/index, /vectors/search, /vectors/rag - Gateway wired with vectord service - Tested: 200 candidate resumes indexed in 5.4s, semantic search + RAG working - 20 unit tests passing (chunker, search, ingestd, shared) - AI gives honest "no match found" when context doesn't support an answer Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
46 lines
1.1 KiB
TOML
46 lines
1.1 KiB
TOML
[workspace]
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resolver = "2"
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members = [
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"crates/shared",
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"crates/proto",
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"crates/storaged",
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"crates/catalogd",
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"crates/queryd",
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"crates/aibridge",
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"crates/ingestd",
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"crates/vectord",
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"crates/gateway",
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"crates/ui",
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]
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[workspace.dependencies]
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tokio = { version = "1", features = ["full"] }
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axum = "0.8"
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serde = { version = "1", features = ["derive"] }
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serde_json = "1"
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tracing = "0.1"
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tracing-subscriber = { version = "0.3", features = ["env-filter", "json"] }
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thiserror = "2"
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uuid = { version = "1", features = ["v4", "serde"] }
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chrono = { version = "0.4", features = ["serde"] }
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tower-http = { version = "0.6", features = ["cors", "trace"] }
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object_store = { version = "0.12", features = ["aws"] }
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arrow = "55"
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parquet = { version = "55", features = ["arrow", "async"] }
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datafusion = "47"
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bytes = "1"
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futures = "0.3"
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sha2 = "0.10"
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url = "2"
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tonic = "0.13"
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prost = "0.13"
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tonic-build = "0.13"
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opentelemetry = "0.28"
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opentelemetry_sdk = { version = "0.28", features = ["rt-tokio"] }
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opentelemetry-stdout = { version = "0.28", features = ["trace"] }
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tracing-opentelemetry = "0.29"
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toml = "0.8"
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csv = "1"
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lopdf = "0.35"
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encoding_rs = "0.8"
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