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Improve dataset generation for next guide.

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Dylan Couzon
2026-04-23 23:07:32 -04:00
parent 0e6e3e3c5c
commit 5ca06869c2
1 changed files with 4 additions and 2 deletions
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qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality-golden-set.md
+4 -2
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@@ -62,12 +62,13 @@ Document:
The evaluation runs in three steps: load the labeled queries into the shape ranx expects, run each through Qdrant, then compute metrics.
**1. Load and assemble.** For each labeled query, build an entry with `query_id`, `query_vector` (embedded with the same model your Qdrant collection uses), and `labels`:
**1. Load and assemble.** For each labeled query, build an entry with `query_id`, `query_text`, `query_vector` (embedded with the same model your Qdrant collection uses), and `labels`:
```python
{
"query_id": "q1",
"query_vector": [0.12, -0.48, 0.33, ...], # embedding of the query text
"query_text": "how does X work",
"query_vector": [0.12, -0.48, 0.33, ...], # embedding of query_text
"labels": {"doc_42": 1}, # source doc for synthetic queries, relevant docs otherwise
}
```
@@ -91,6 +92,7 @@ golden_set = []
for i, item in enumerate(labeled_data):
golden_set.append({
"query_id": f"q{i}",
"query_text": item["query_text"],
"query_vector": embed(item["query_text"]),
"labels": item["labels"],
})