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Get Started Free →datadrivenconstruction/semantic-search-cwicr
Semantic search in DDC CWICR construction database using vector embeddings. Find similar work items and resources for cost estimation.
Passed 24 of 24 test cases with the skill, and 8 of 24 without it.
and 15 more in the full report
16 fixed8 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 16 test cases the skill fixed. Same model, same prompt, with and without the skill.
We are initializing our Qdrant database collections for the CWICR dataset. What exact collection name string must be used for the Chinese language edition database?
| Checked | Without skill | With skill |
|---|---|---|
| Uses collection name cwicr_zh_v3. | Missed | Passed |
The instructions your model reads when the skill loads.
Construction cost estimation requires finding relevant work items from large databases. Traditional keyword search fails when:
DDC CWICR provides pre-computed embeddings (BAAI/bge-m3, 1024 dimensions) enabling multilingual semantic search across 8 national bases (78,228 positions) plus the 30-market global base in 26 languages, with 48 PPP-repriced market catalogs per national base.
| National base | Region id | Positions |
|---|---|---|
| Turkey (Birim Fiyat) | TR_NATIONAL | 22,704 |
| China (Beijing Dinge + Bole) | ZH_CHINA | 11,312 |
| Brazil (SINAPI) | BR_NATIONAL | 9,723 |
| Spain (BCCA Andalucía) | ES_ANDALUCIA | 6,453 |
| Italy (Prezzario Toscana) | IT_TOSCANA | 5,836 |
| Vietnam (Dinh Muc) | VN_NATIONAL | 4,299 |
| Indonesia (AHSP) | ID_NATIONAL | 2,784 |
| Greece (GGDE) | GR_NATIONAL | 2,647 |
Each base ships the 95-column CWICR master schema (rate_code, rate_original_name, rate_final_name, rate_unit, total_cost_per_position, classification hierarchy collection/department/section/subsection/category, resource_* component lines with is_material/is_machine/is_labor flags) plus 26 language editions and 48 markets/*.csv catalogs.
Latest data release: v0.4.0 (see releases).
pip install qdrant-client pandas sentence-transformers
test cases passed, vs 8 / 24 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.