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cgoudet
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Mar 10, 2026
| Returns a list of (chunk, distance) tuples. Chunk includes source_file | ||
| via the ORM relation for display (e.g. source_file.doi). | ||
| """ | ||
| if k <= 0: |
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Plutot un message d'erreur?
| return [] | ||
| query_vector = embed_query(query) | ||
| qs = ( | ||
| DocumentChunk.objects.filter(embedding__isnull=False) |
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Est ce que postgres a un système d'ANN? plutot que de tester toutes les lignes?
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Summary
search_chunks(query, k)returns the top‑k chunks by pgvector cosine distance; only chunks with a stored embedding are considered.embed_query()inembedding.py, newsearch.pywithsearch_chunks()using pgvector CosineDistance andselect_related("source_file").DATABASE_URL=...@localhost:5432/...works for local and test runs.Scope
Semantic search implementation and its tests only. No UI for search yet; no real-PDF E2E test.