What Data for This Category Looks Like
Data sources include public project approval documents from transportation authorities, project completion archives, operation and maintenance logs, and real-time road network monitoring data. Update cadence varies by project phase: full one-time update upon completion for new construction projects; monthly updates for maintenance data and annual updates for operation metrics for operational sections. Document structure includes four modules: basic project information, engineering parameters, operation data, and compliance approval documents. Fields include route mileage (unit: km), designed speed (unit: km/h), total investment (unit: ten thousand yuan), over-limit detection times (unit: times/quarter), among others. The data mixes structured tables and unstructured text content.
What Constraints These Characteristics Impose on Model Integration and Configuration
Multi-source mixed data formats require configuration support for multi-type document parsing, to adapt to PDF completion reports, Excel logs, JSON monitoring data and other formats. Long documents with extensive technical terms require adjustment of text chunking parameters to avoid breaking term integrity. Inconsistent field units require configuration of entity extraction unit verification rules to ensure data standardization. Content with different update cycles requires configuration of incremental sync trigger intervals to avoid excessive resource consumption from full re-runs. Old project documents in scanned format require enabling OCR parsing configuration to extract valid text.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxChunkSize | 800–1200 characters | Railway and highway due diligence reports contain extensive engineering parameters and long sentence descriptions. This range avoids breaking technical term integrity during chunking |
similarityThreshold | 0.72–0.78 | Matching technical terms and standardized fields requires balancing recall accuracy and coverage, which this range achieves |
recallTopK | Top 6–8 entries | Due diligence reports require full lifecycle project data coverage. This range avoids context overload while covering key compliance items |
PARSE_OCR_ENABLE | Enabled | Some old project due diligence reports are scanned PDF files. OCR recognition is needed to extract parsable text |
SYNC_INTERVAL_HOURS | 24 hours | Maintenance data for operational sections is updated monthly. This interval ensures data timeliness while reducing API call frequency |
API_TIMEOUT | 600 seconds | Parsing large drawing attachments or merging multi-source data requires extended timeout to prevent task interruptions |
The parameter values provided on this page are all conventional recommendations for establishing configuration starting points. Actual values are affected by material format, data volume and business rules. Specific issues require targeted analysis. Testing on local samples is recommended before finalizing settings.
Three Common Mistakes
- Symptom: Calling a local large model returns a
connection refusederror code, preventing successful access. Cause: The local model’s API address was not filled in correctly, or access permissions for the local port were not opened, blocking connection establishment. - Symptom: Knowledge base question and answer results do not include cited document details. Cause: The associated citation configuration item was not enabled, or recalled documents were not correctly linked to the context fragments used in the model’s response.
- Symptom: After configuring a text understanding model in version 4.9.0, entity extraction result fields are empty. Cause: The correct task type was not specified in the model integration configuration, or the entity extraction format supported by the corresponding model was not matched.
How to Confirm Configuration Is Complete
- A typical railway and highway project due diligence report is uploaded, and parsed text chunks are checked for complete engineering parameter paragraphs.
- The published API interface is called, and returned results are inspected for model responses and associated document citation information.
- Scheduled synchronization task running logs are reviewed, and incremental data updates at the set interval are confirmed.
- Local large model access is tested, and an interface debugging tool is used to verify that requests return normal model-generated content.
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.