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>
28 lines
807 B
TOML
28 lines
807 B
TOML
[package]
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name = "gateway"
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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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catalogd = { path = "../catalogd" }
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queryd = { path = "../queryd" }
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aibridge = { path = "../aibridge" }
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ingestd = { path = "../ingestd" }
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vectord = { path = "../vectord" }
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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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tracing-subscriber = { workspace = true }
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tower-http = { workspace = true }
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object_store = { workspace = true }
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proto = { path = "../proto" }
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tonic = { workspace = true }
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opentelemetry = { workspace = true }
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opentelemetry_sdk = { workspace = true }
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opentelemetry-stdout = { workspace = true }
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tracing-opentelemetry = { workspace = true }
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