What the data for this category looks like
The investment research data for building construction engineering mainly comes from building design drawings, construction logs, material test reports, bidding documents, industry quota standards, and project cost documents. The data update rhythm fluctuates with project construction milestones. New documents are generated concentratedly during phases including basic construction, main structure topping, and completion settlement. Incremental data such as daily material price updates and specification revisions is synced on a regular basis. Document structures include structured bills of quantities, material model lists, long-text construction plans, and technical disclosure records. Fields include project number, floor area, material specification, construction date, and others. Units include square meters, cubic meters, tons, yuan per square meter, and other engineering-specific units.
What constraints these characteristics impose on vector models and indexing
Building construction engineering data has both structured and unstructured characteristics, requiring vector models to adapt to both numeric fields and semantic encoding of professional text. Document lengths vary widely, from a few lines of test reports to dozens of pages of construction plans, so the indexing system needs adaptive segmentation capabilities. Data updates surge concentratedly with project milestones, leading to sharp increases in indexing task volume during peak periods, so the system must support elastic scaling to avoid blocking. High-frequency occurrence of professional terms and specialized units requires the vector model to have industry semantic understanding capabilities, otherwise professional term matching deviations will occur. The need for unit association with structured fields also requires additional association of field metadata during the indexing phase to improve retrieval accuracy.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
segment_length | 800–1200 characters | Building construction engineering documents include long-text construction plans and structured lists. This range balances semantic integrity and retrieval accuracy, avoiding professional term breakage from excessive segmentation |
recall_count | Top 10–15 results | Building construction engineering investment research needs to cover multi-dimensional information including materials, construction, and cost. This value can cover core retrieval needs while controlling context load |
similarity_threshold | 0.65–0.8 | Building construction engineering has dense professional terms. This threshold filters low-relevance results while retaining valid matches for approximate expressions within the industry |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large building design drawings takes a long time. This duration adapts to the parsing and vectorization processing flow for long documents |
index_batch_size | 50–100 items/batch | The data volume of single building construction engineering documents varies significantly. This batch size balances indexing efficiency and cluster resource usage, avoiding node overload caused by excessively large single batches |
embedding_model | Fine-tuned vector model for construction engineering domain | Building construction engineering contains a large number of professional terms and specialized units. Fine-tuned models can improve the encoding accuracy of professional semantics and adapt to industry-specific retrieval needs |
The parameter values provided on this page are all conventional recommendations used to determine a starting point for configurations. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- The knowledge base dataset remains in the "indexing" state for an extended period after import. This occurs because
index_batch_sizeis not adjusted to accommodate large-volume building construction engineering documents. Excessively large batches deplete node resources, blocking indexing processes. - Semantic similarity values appear abnormally high after switching vector models. This occurs because the scoring rules of the new model are not adapted. Some models use a 10000-point similarity calculation system, and the default 0-1 threshold cannot correctly filter results.
- Indexing speed fails to meet project milestone update requirements. This occurs because multi-replica deployment is not enabled, and cluster resources are not used to process indexing tasks in parallel, resulting in insufficient single-node processing efficiency.
How to confirm configurations are set correctly
- Upload a single typical building construction engineering document, review the parsed segmentation results, and confirm the segment length matches the preset configuration.
- Initiate a retrieval test, verify the recall count matches the configured value, and check that the semantic relevance of returned results meets business requirements.
- View indexing process logs, confirm there are no timeout errors, and that batch processing progress aligns with expectations.
- Compare similarity scores across different vector models, adjust
similarity_thresholdto filter invalid results, and ensure retrieval result accuracy meets investment research requirements.
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.