What the Data for This Category Looks Like
Railway and highway intelligent due diligence data sources include public bidding documents, project completion acceptance reports, industry statistical data released by transportation authorities, and daily inspection logs of operation and maintenance units. There are three update cycles:
- New projects are updated upon completion acceptance
- Operation and maintenance data is updated quarterly
- Existing regular projects are updated annually
A single due diligence report typically includes six sections: project overview, technical parameters, investment details, construction milestones, operation and maintenance records, and compliance documents. Core fields are:
- Main line mileage (unit: kilometers)
- Designed speed (unit: kilometers per hour)
- Cumulative bridge and tunnel length (unit: kilometers)
- Annual traffic volume (unit: standard vehicle trips)
- Completion date (format: YYYY-MM-DD)
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
The multi-field, multi-unit characteristics of railway and highway due diligence data require that each follow-up question in multi-turn dialogue clearly specifies the field unit. This avoids confusion between main line mileage and branch line mileage, or between annual traffic volume and daily traffic volume.
Differences in data update cycles require multi-turn dialogue to automatically identify the project’s update cycle and call the latest version of the corresponding data source.
The large content volume of a single report requires multi-turn dialogue to limit the length of context recall, to avoid exceeding the model’s processing limit.
Compliance documents must be associated with specific clauses, so multi-turn dialogue must track previously mentioned milestones to avoid missing key compliance requirements.
Prompts must predefine standard naming and units for fields to reduce ambiguity in subsequent conversations.
How to Configure the Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Railway and highway single reports have large content volume, requiring a balance between context recall and model processing capacity |
recallTopK | Top 6–8 entries | Avoid retrieving too many irrelevant historical dialogues or document fragments, focusing on core technical parameters and compliance clauses |
similarityThreshold | 0.72–0.80 | Distinguish the similarity between technical parameters and operation and maintenance records, avoiding confusion of mileage data with different units |
fileParseChunkSize | 1500–2000 characters | Adapt to long-paragraph technical parameter descriptions in railway and highway reports, reducing field loss caused by segment truncation |
toolCallMaxRetries | 2 retries | Handle network exceptions or parameter format errors during tool calls, avoiding timeouts caused by repeated calls |
systemPromptTemplate | Clearly specify field units and project types, prioritize calling the latest updated data sources | Predefine constraint rules for dialogue to reduce subsequent ambiguity |
The parameter values provided on this page are common starting points for configuration. 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 Configuration Errors
- A 400 status code (no body) is returned when calling the MySQL tool to obtain operation and maintenance data. Cause: The correct database table name and field unit are not specified in the tool configuration, resulting in incorrect SQL statement splicing or empty dataset returns.
- When switching models during multi-turn dialogue, different models return technical parameters with inconsistent units. Cause: The field unit constraints in the system prompt are not updated synchronously in the model switching configuration, and different models have different parameter parsing rules.
- Historical dialogue records cannot be cleared after deployment via Docker Compose. Cause: No persistent storage volume is mounted, or no cleanup script for dialogue records is configured, resulting in historical data remaining retained after container restart.
How to Confirm Successful Configuration
- Initiate a test dialogue that includes a question about technical parameter units, and verify that the returned results clearly label the units of the corresponding fields and match the fields in the data source.
- Call the configured MySQL tool to obtain operation and maintenance data for a specified project, and check whether the returned status code and field format meet preset requirements.
- Switch the model used for the dialogue, and verify that content returned by different models all follow the preset system prompt constraints.
- Generate a web sharing link, initiate a new dialogue via the link, and check whether the previous conversation history can be properly associated and restored.
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.