What the data for this category looks like
Data sources for railway and highway research reports include public reports from transportation industry research institutions, internal analysis documents from railway and highway operation enterprises, and operation statistics materials released by industry associations. Updates follow a rhythm of monthly and quarterly regular reports, plus temporary supplementary reports for events such as new line openings or policy adjustments. Documents typically include five core modules: line operation overview, passenger and freight volume statistics, cost composition, policy impact analysis, and future planning. Fields include average daily passenger volume, per-kilometer operation cost, and total line length. Units are mostly quantitative metrics such as passenger trips, yuan, and kilometers. Some documents include structured operation data tables.
Constraints on Knowledge Base Retrieval and Recall
Dispersed data sources require connecting multiple storage types, increasing complexity of incremental updates and metadata management. The combined regular and temporary update rhythm requires the retrieval system to support on-demand incremental synchronization, avoiding full repeated parsing. Large numbers of quantitative fields and structured tables in documents require the retrieval pipeline to support both precise matching of numerical fields and text semantic matching. Relying solely on text semantic matching cannot cover quantitative retrieval needs. Core information is concentrated in operation data and policy analysis modules. Recall results must prioritize these high-value chapters to avoid irrelevant content interfering with retrieval effectiveness. Individual research reports are typically lengthy. Numerical association must be preserved when splitting documents, otherwise retrieval accuracy will decrease.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Railway and highway research reports contain long sections of operation data and policy explanations. Splitting too short breaks numerical associations, while splitting too long prevents precise matching of search terms |
similarityThreshold | 0.72–0.85 | Numerical fields in research reports require high matching accuracy to avoid irrelevant results, while covering wording differences across same-category reports |
reRankTopN | Top 6–8 results | Core information is concentrated in operation and policy modules. Too many recall results increase large model organization burden, while too few miss key relevant content |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Individual research reports may include multiple pages of operation reports, with longer parsing time than general documents. Timeouts cause upload failures |
enableIncrementalUpdate | Enabled | Research reports have regular updates and temporary event reports. Incremental updates reduce repeated parsing and storage overhead |
UPLOAD_FILE_MAX_SIZE | 500 MB | Individual large research reports may contain multi-period operation data collections, requiring allowance for larger file uploads |
The parameter values provided on this page are common recommendations used as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Symptom: Knowledge base search takes more than 15 seconds, and retrieval for individual research reports is laggy. Cause:
chunkSizeand recall count are not adjusted for long documents. Excessive splitting or too many recalled results overload the retrieval pipeline. - Symptom: After offline deployment upgrade, knowledge base queries return invalid content. Cause:
similarityThresholdandreRankTopNparameters were not recalibrated after upgrade. Default configurations cannot adapt to the numerical field characteristics of railway and highway research reports. - Symptom: When the knowledge base has no matching content, the AI still returns fabricated answers. Cause: The
disableFictionWhenNoMatchconfiguration is not enabled, and the trigger logic for empty retrieval results is not associated.
How to Confirm Configuration is Valid
- Upload a standard railway and highway research report, check the parsed text splitting results, confirm the splitting logic matches the category's document characteristics.
- Enter search terms containing specific operation indicators, verify the matching degree of recalled results, confirm the threshold adapts to category requirements.
- Simulate a scenario with no matching retrieval results, confirm the AI does not return fabricated answers, verify the configuration takes effect.
- Upload multiple research reports of different sizes, confirm the upload process has no errors, verify the upload configuration is reasonable.
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.