What does the data for this category look like?
Data sources for building construction engineering investment research include industry construction standards released by the Ministry of Housing and Urban-Rural Development, project bidding documents, construction drawing PDF files, building material supplier quotation sheets, weekly engineering progress reports, and cost quota documents. Update frequencies vary by scenario: Bidding documents are added in real time as projects progress. Cost quotas are updated quarterly or annually. Industry standards are revised every 2-3 years. Document structures include structured quota tables, unstructured technical documentation, and engineering measurement Excel files with professional units. Fields cover building area, project cost, material unit price, and similar metrics. Units follow engineering standard units such as square meters, ten thousand yuan, and yuan/ton.
What constraints do these characteristics impose on model access and configuration?
The multi-type documents and professional unit requirements for building construction engineering require the model access link to adapt to parsing logic for both structured quota tables and long-text construction drawings. Real-time updated bidding documents need high-frequency batch embedding, which creates constraints on concurrency and batch parameters for embedding tasks. Bidding documents for multilingual overseas projects require the embedding model to support vector mapping of multilingual professional terms, to avoid recall bias. Professional units and field formats of engineering data require the configuration link to enable parameter verification. This prevents fields with non-standard units from being incorrectly embedded, which would harm subsequent investment research retrieval accuracy.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
EMBEDDING_BATCH_SIZE | 16-32 items/batch | Building construction engineering documents are mostly long text. Excessive batch size will trigger model rate limiting, while too small a batch size reduces embedding efficiency |
MAX_SEGMENT_LENGTH | 800-1200 characters | Technical parameter paragraphs of building construction drawings fall within this range, to avoid splitting that disrupts professional term integrity |
SIMILARITY_THRESHOLD | 0.75-0.82 | Investment research scenarios require precise matching of professional terms. A threshold that is too low will introduce irrelevant engineering data |
RECALL_TOP_K | Top 8-12 results | Building construction investment research needs to cover multi-dimensional cost, progress, and standard data. Too many results will increase context redundancy |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large-scale construction drawing PDF files take a long time to parse, to avoid task interruption due to timeout mid-process |
NO_THINK | true (question answering only) | Investment research scenarios require direct return of structured professional data, without additional model reasoning steps |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing the configuration.
Three Common Misconfigurations
- Multilingual bidding document recall rate fails to meet standards after batch re-embedding. The multilingual adaptation parameters of the embedding model are not adjusted for building construction engineering professional terms. Only the model is replaced, and professional vocabulary mapping is not enabled.
- A
400 Bad Requesterror is returned when calling the model. TheNO_THINKparameter is not configured in the request body of the question and answer calling interface, and is mistakenly written into the knowledge base embedding configuration items. - The SaaS version model call interface displays a test status. The model permission binding for the building construction investment research scenario is not completed, or the billing item corresponding to the scenario is not checked in the billing configuration.
How to Confirm Successful Configuration
- Upload a single construction drawing PDF file with more than 100 pages. Check the matching degree between the number of parsed segments and the actual number of document paragraphs. Adjust
MAX_SEGMENT_LENGTHuntil the segmentation logic conforms to the professional document structure. - Initiate a retrieval request for multilingual bidding documents. Check whether the recall results include professional terms in the corresponding language, to verify that the multilingual adaptation configuration of the embedding model takes effect.
- View the model call log to confirm that the
NO_THINKparameter is correctly included in the question and answer request, with no missing parameters or incorrect position. - Submit a batch embedding task. Check that the task progress bar matches the actual number of documents, to confirm that the
EMBEDDING_BATCH_SIZEconfiguration does not trigger platform rate limiting.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.