Add dataset card

README.md

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+---
+license: apache-2.0
+language:
+  - ar
+  - bg
+  - cs
+  - de
+  - en
+  - es
+  - fr
+  - hi
+  - hr
+  - id
+  - it
+  - ja
+  - ko
+  - nl
+  - pl
+  - pt
+  - ro
+  - ru
+  - sv
+  - th
+  - tr
+  - vi
+  - zh
+tags:
+  - construction
+  - cost-estimation
+  - BIM
+  - quantity-surveying
+  - vector-database
+  - qdrant
+  - bge-m3
+  - hybrid-search
+  - multilingual
+  - construction-rates
+size_categories:
+  - 1M<n<10M
+pretty_name: CWICR Vector DB (BGE-M3 V3) — 30 Countries
+---
+
+# CWICR Vector Database — BGE-M3 V3 Snapshots
+
+Production Qdrant snapshots for **CWICR (Construction Works Items, Costs & Resources)** — a multilingual catalogue of construction rate databases covering **30 countries / language locales**. Each snapshot encodes one country's rate book using the **BAAI/bge-m3** embedder and is ready to restore directly into a Qdrant server for hybrid semantic search.
+
+These snapshots are the V3 production artifacts produced by the OpenConstructionEstimate / CWICR pipeline. The primary downstream use case is **BIM-element ↔ construction-rate matching** for cost estimation, but the data is general-purpose for any retrieval over multilingual construction-cost catalogues.
+
+## What's inside
+
+Each `.snapshot` file is a Qdrant collection snapshot of one country's rate database with:
+
+- **Granularity**: one Qdrant point per unique `rate_code` (≈ 55,000 rates per country).
+- **Named vectors** per point:
+  - `dense` — 1024-d, COSINE, BGE-M3 dense embedding of the rate's title + work composition + hierarchy.
+  - `sparse` — BGE-M3 lexical sparse vector (BM25-like, IDF-modified) for exact-code / token matching.
+  - `resources` — 1024-d, COSINE, optional dense embedding of the rate's resource list (machinery, materials, labour). Present only for non-abstract rates.
+- **Payload** (29 indexed fields): `rate_code`, `country`, `collection_name`, `category_type`, `department_code`, `is_abstract`, `rate_unit`, `mass_*`, plus hierarchy / classification tags. Heavy fields (prices, full resource composition) live in the matching `*.parquet` files in the parent CWICR repository, looked up by `rate_code`.
+
+Total rates indexed: **~1.65 M points** across 30 collections.
+
+## File naming
+
+```
+<LANG>_<CITY>_workitems_costs_resources_EMBEDDINGS_BGEM3_V3_DDC_CWICR.snapshot
+```
+
+Example: `VI_HANOI_workitems_costs_resources_EMBEDDINGS_BGEM3_V3_DDC_CWICR.snapshot`.
+
+Snapshots are organised into one folder per language code (`AR/`, `BG/`, `CS/`, `DE/`, `EN/`, `ES/`, `FR/`, `HI/`, `HR/`, `ID/`, `IT/`, `JA/`, `KO/`, `MX/`, `NG/`, `NL/`, `NZ/`, `PL/`, `PT/`, `RO/`, `RU/`, `SV/`, `TH/`, `TR/`, `UK/`, `US/`, `VI/`, `ZA/`, `ZH/`, `AU/`).
+
+## Coverage status
+
+This dataset is being uploaded in stages. Current state on the Hub:
+
+| Status | Locales |
+|---|---|
+| Uploaded here | VI (Hanoi), ZA (Johannesburg), ZH (Shanghai) |
+| Pending migration | the remaining 27 locales, currently hosted as Git LFS in the source repo |
+
+The remaining snapshots will be migrated as bandwidth allows. The encoder and source parquet files are open and deterministic — see "Reproducibility" below for regenerating any locale from scratch.
+
+## Reproducibility
+
+Each snapshot is reproducible from the corresponding parquet (`*_workitems_costs_resources_DDC_CWICR.parquet`) in the [CWICR source repo](https://github.com/datadrivenconstruction/OpenConstructionEstimate-DDC-CWICR) using the V3 production encoder in that repo: `0_Workflow and Pipelines CWICR/python/10-embedding-pipeline/v3_production_all_languages.py`.
+
+Embedder: `BAAI/bge-m3` (Apache-2.0). Point IDs are derived via `uuid5(NS_CWICR, f"{country}|{rate_code}")` so re-encoding is idempotent.
+
+## Restoring a snapshot into Qdrant
+
+Bring up Qdrant locally:
+
+```bash
+docker run -d --name qdrant -p 6333:6333 -p 6334:6334 \
+  -v "$(pwd)/qdrant_storage:/qdrant/storage" qdrant/qdrant:latest
+```
+
+Upload and restore the snapshot via the HTTP API:
+
+```bash
+COLL=cwicr_vi  # one collection per locale: cwicr_vi, cwicr_za, cwicr_zh, ...
+SNAPSHOT=VI_HANOI_workitems_costs_resources_EMBEDDINGS_BGEM3_V3_DDC_CWICR.snapshot
+
+# 1. Create empty collection (config will be overwritten by the snapshot)
+curl -X PUT "http://localhost:6333/collections/$COLL" \
+  -H 'Content-Type: application/json' \
+  -d '{"vectors": {}}'
+
+# 2. Upload + recover
+curl -X POST "http://localhost:6333/collections/$COLL/snapshots/upload?wait=true" \
+  -F "snapshot=@$SNAPSHOT"
+```
+
+Or programmatically with `qdrant-client`:
+
+```python
+from qdrant_client import QdrantClient
+client = QdrantClient(url="http://localhost:6333")
+client.recover_snapshot(
+    collection_name="cwicr_vi",
+    location="file:///abs/path/to/VI_HANOI_..._BGEM3_V3_DDC_CWICR.snapshot",
+)
+```
+
+## Hybrid search example
+
+```python
+from FlagEmbedding import BGEM3FlagModel
+from qdrant_client import QdrantClient, models
+
+model = BGEM3FlagModel("BAAI/bge-m3", use_fp16=True)
+qdrant = QdrantClient(url="http://localhost:6333")
+
+query = "máy đào 15 m³"   # Vietnamese: "15 m³ excavator"
+enc = model.encode([query], return_dense=True, return_sparse=True)
+q_dense = enc["dense_vecs"][0].tolist()
+q_sparse = enc["lexical_weights"][0]
+
+hits = qdrant.query_points(
+    collection_name="cwicr_vi",
+    prefetch=[
+        models.Prefetch(query=q_dense, using="dense", limit=100),
+        models.Prefetch(
+            query=models.SparseVector(
+                indices=[int(i) for i in q_sparse.keys()],
+                values=list(q_sparse.values()),
+            ),
+            using="sparse",
+            limit=100,
+        ),
+    ],
+    query=models.FusionQuery(fusion=models.Fusion.RRF),
+    limit=10,
+    with_payload=True,
+)
+for p in hits.points:
+    print(p.score, p.payload["rate_code"], p.payload.get("collection_name"))
+```
+
+For BIM-element matching, queries are typically built from IFC entity properties (type, dimensions, material). Add a third prefetch over the `resources` named vector when the query is best matched by machinery/material lists rather than rate titles.
+
+## Languages and locales
+
+23 distinct human languages across 30 locale-specific rate books (some languages cover multiple countries with locale-specific pricing and classification, e.g. EN-CA / EN-GB / EN-US / EN-AU / EN-NZ / EN-NG / EN-ZA, ES-ES / ES-MX, PT-BR). Each locale uses its own native-language collection structure (Russian СНиП codes, German DIN, US MasterFormat / RSMeans-style classifiers, etc.), preserved verbatim in the payload.
+
+## License
+
+Snapshots are released under **Apache-2.0**, matching the license of BGE-M3 and Qdrant. The upstream rate data is sourced from publicly available national/regional rate catalogues; license applicability of underlying rate codes follows each catalogue's terms of use.
+
+## Citation
+
+If you use these snapshots in academic work please cite the parent repository:
+
+```
+@software{cwicr_2026,
+  author = {Boiko, Artem and DataDrivenConstruction},
+  title  = {OpenConstructionEstimate — CWICR: Multilingual Construction Rates Vector Database},
+  year   = {2026},
+  url    = {https://github.com/datadrivenconstruction/OpenConstructionEstimate-DDC-CWICR}
+}
+```
+
+## Contact
+
+- Source code & issue tracker: https://github.com/datadrivenconstruction/OpenConstructionEstimate-DDC-CWICR
+- Author: Artem Boiko (DataDrivenConstruction)