--- 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 πŸ’‘ **Looking for a full estimation platform?** These snapshots power the semantic-search layer of [**OpenConstructionERP**](https://github.com/datadrivenconstruction/OpenConstructionERP) β€” an open-source modular ERP for construction (BOQ editor, BIM/CAD import, AI takeoff, GAEB XML, AGPL-3.0). The ERP boots one Qdrant collection per locale from these files and uses BGE-M3 hybrid search to match BIM elements to local rates in 30 markets out of the box. ## 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` (β‰ˆ 55,000 rates per country). - **Named vectors** per point: - `dense` β€” 1024-d, COSINE, BGE-M3 dense embedding of the rate's title + work composition + hierarchy. - `sparse` β€” BGE-M3 lexical sparse vector (BM25-like, IDF-modified) for exact-code / token matching. - `resources` β€” 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 ``` __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 **All 30 locales are uploaded.** One Qdrant snapshot per locale, organised in `/` folders: `AR/`, `AU/`, `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/`. For the tabular source data (parquet + CSV/XLSX catalogues + localised TXT readmes + per-country README.md) used to produce these snapshots, see the companion dataset: [`DataDrivenConstruction/cwicr-construction-rates`](https://huggingface.co/datasets/DataDrivenConstruction/cwicr-construction-rates) β€” 150 country files + top-level docs, fully mirrored 1-to-1 with this snapshot repo. ## 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 = "mΓ‘y Δ‘Γ o 15 mΒ³" # Vietnamese: "15 mΒ³ 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 БНиП codes, German DIN, US MasterFormat / RSMeans-style classifiers, etc.), preserved verbatim in the payload. ## Related artifacts - **Tabular source** (parquet + catalogues + per-country PDFs) β€” used to build these snapshots and recommended for any work that needs raw numeric data: β†’ [`DataDrivenConstruction/cwicr-construction-rates`](https://huggingface.co/datasets/DataDrivenConstruction/cwicr-construction-rates) - **OpenConstructionERP** β€” open-source ERP that consumes these snapshots in production: β†’ [`github.com/datadrivenconstruction/OpenConstructionERP`](https://github.com/datadrivenconstruction/OpenConstructionERP) - **Source code / pipelines** (encoders, classifiers, search demos, BIM matchers): β†’ [`github.com/datadrivenconstruction/OpenConstructionEstimate-DDC-CWICR`](https://github.com/datadrivenconstruction/OpenConstructionEstimate-DDC-CWICR) ## Support the project ⭐ If this dataset is useful to you β€” or if you'd like to see more open construction databases like it β€” the most helpful thing you can do is **leave a star on the [OpenConstructionERP repository](https://github.com/datadrivenconstruction/OpenConstructionERP)**. Stars are how we gauge demand and prioritise the next country / language / source to publish. No account needed beyond GitHub, no signup, no money β€” just a one-click thank-you that directly steers what gets built next. ## 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 β€” 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)