What the data for this category looks like
Ordnance equipment-related yield and market trend data comes from publicly disclosed operating data of the national defense and military industry sector and secondary market trading trend data. The update rhythm is daily updates for core market fields, and full operating indicators are updated quarterly. The document structure is mostly structured CSV or JSON format, including fields such as: military equipment category identifier, statistical date, asset yield value, affiliated group code, and daily total trading volume. For field units: the asset yield value is a dimensionless ratio, the daily total trading volume is measured in shares, and the statistical date uses the YYYY-MM-DD format.
What constraints do these characteristics impose on the model access and configuration workflow
Daily updated market trend data requires model call APIs to support high-frequency pulling. An overly long cache cycle is not recommended, as it will cause broadcast data to lag behind current day's market trends. Structured CSV/JSON format data requires that the model's prompt clearly specifies field parsing rules to avoid unstructured natural language output that affects automated processing of subsequent broadcasts. There are many subcategory identifiers for military equipment categories, requiring configuration of multi-dimensional field filtering parameters to ensure the model can accurately match yield data for specified categories. Publicly disclosed operating data has publication lag, requiring configuration of a data verification link to filter non-current-day delayed data and ensure the timeliness of broadcast content.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Ordnance equipment yield data contains multiple fields, requiring sufficient context to accommodate parsing rules and historical data examples |
apiRequestTimeout | 30 seconds | Market trend data interfaces respond quickly. An overly long timeout will cause broadcast delays, while an overly short timeout will miss valid data pulls |
promptTemplate | Output in a structured format of "statistical date + category identifier + yield value + trading data", retain specified fields | Ordnance equipment data has many fields and requires accurate broadcasting. A clear template prevents the model from omitting key information |
dataSourceCacheTTL | 3600 seconds | Daily updated market trend data does not require long-term caching. A 1-hour cache balances timeliness and API call frequency |
responseFormat | JSON | This is structured data for subsequent reporting, and JSON format facilitates front-end rendering and multi-turn dialogue transfer |
fieldFilterRule | Only retain records with a statistical date of the current day | Meets timeliness requirements for daily reporting, filters delayed historical data |
The parameter values provided on this page are common recommended starting points for configuration. The actual values are affected by material form, data volume and business rules. Specific issues require case-by-case analysis. Testing on internal samples is recommended before finalizing the settings.
Three common configuration errors
- API calls return
403 status code (no body). The cause is that the API key permission scope is not configured correctly, or the whitelist IP of the corresponding interface is not added in the FastGPT channel configuration. - The model fails to correctly identify space-separated fields in the prompt. The cause is that the prompt does not explicitly specify that spaces are used as field separators, causing the model to treat consecutive spaces as meaningless characters.
- The model output omits yield data fields. The cause is that correct field filtering conditions are not configured in
fieldFilterRule, causing the model to filter out necessary valid fields.
How to confirm the configuration is complete
- Run a test call, and verify that the returned result fields match the configured
fieldFilterRule. - Review API call logs to confirm that each call's response time does not exceed the configured
apiRequestTimeoutvalue. - Compare the input data source with the model output content to confirm that the output format meets the configured
responseFormatrequirements. - Run another call after a 1-hour interval, check if the returned data is the latest current day's market trend data, and confirm that the cache configuration is effective.
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.