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49 lines
1.8 KiB
Markdown
49 lines
1.8 KiB
Markdown
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# Search
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OmniGraph runs vector, full-text, and hybrid search in the same runtime as graph
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traversal — a single [query](../queries/index.md) can combine a vector `nearest`,
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a `bm25` text score, and an `Expand` traversal. Search functions are used inside
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`match` (to filter), or as expressions inside `return` / `order` (to score and
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rank).
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## Functions
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| Function | Purpose | Backing index |
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|---|---|---|
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| `nearest($x.vec, $q)` | k-NN vector search (cosine) | vector index (IVF / HNSW) |
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| `search(field, q)` | Generic full-text search | inverted (FTS) index |
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| `fuzzy(field, q [, max_edits])` | Levenshtein-tolerant text search | inverted index |
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| `match_text(field, q)` | Pattern match | inverted index |
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| `bm25(field, q)` | BM25 relevance scoring | inverted index |
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| `rrf(rank_a, rank_b [, k])` | Reciprocal Rank Fusion of two rankings (default `k=60`) | fuses scored rankings |
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- `nearest()` requires a `limit`. The query vector is resolved from the param map,
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or embedded from a text input at runtime via the configured
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[embedding client](embeddings.md).
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- Scores and ranks propagate as ordinary columns, so you can `return` a score and
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`order` by it.
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## Hybrid ranking with `rrf`
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Reciprocal Rank Fusion combines two independent rankings (typically one vector and
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one text) into a single fused ranking, without needing the two score scales to be
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comparable. Rank each retrieval separately, then fuse:
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```gq
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query hybrid($q: String) {
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match { $d: Document { } }
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return {
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$d,
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rrf( nearest($d.embedding, $q), bm25($d.body, $q) ) as score
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}
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order { score desc }
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limit 10
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}
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```
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## Indexes and embeddings
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Search functions only work when the backing index exists — see
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[indexes](indexes.md) for building vector and inverted indexes, and
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[embeddings](embeddings.md) for generating the vectors `nearest` searches over.
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