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>
15 lines
386 B
JSON
15 lines
386 B
JSON
{
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"id": "75bb6855-488b-4300-89c2-970871bd99cc",
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"name": "email_log",
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"schema_fingerprint": "auto",
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"objects": [
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{
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"bucket": "data",
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"key": "datasets/email_log.parquet",
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"size_bytes": 1873775,
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"created_at": "2026-03-27T13:11:42.757205427Z"
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}
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],
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"created_at": "2026-03-27T13:11:42.757211105Z",
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"updated_at": "2026-03-27T13:11:42.757211105Z"
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} |