What the Data for This Category Looks Like
This category of data is primarily sourced from licensed financial information service APIs, internal institutional transaction ledgers, and publicly disclosed regulatory documents. Full updates are completed within 1 hour after market close on trading days, and delayed to before the opening of the next trading day on non-trading days. The data uses a structured format, including product unique identifier, daily return value, cumulative return value, and data effective timestamp. The fields include product_id, daily_return_value, cumulative_return_value, data_time, and the units of return-related fields are yuan.
Constraints Imposed by These Characteristics on Model Integration and Configuration
Configure cross-source data alignment parameters to avoid field mapping deviations, due to multi-source data sources. Set scheduled model invocation tasks, with timeout thresholds matching the data update cycle, to align with the fixed update rhythm. Configure field filtering and mapping rules to ensure the model only calls valid fields, given the large number of structured fields. Configure date filtering parameters to prevent the introduction of expired or non-target period data, per the time range limitation of daily report data. Configure format verification rules for model outputs to avoid content that does not meet scenario requirements, to meet data compliance needs.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
trigger_mode | Scheduled trigger, 1-day interval | Matches the daily update rhythm of daily reports, avoids repeated calls or missed updates |
data_filter_days | 1 | Only retains data from the current day, aligns with daily report broadcast scenario requirements |
field_mapping_rules | Map according to product_id, daily_return_value, data_time | Matches the standard field structure of this category of data, reduces preprocessing costs |
model_timeout | 600 seconds | Covers the total time of multi-source data pulling and model inference, avoids mid-run timeout interruptions |
response_format | JSON format, containing product list and return details | Adapts to the structured input requirements of broadcast tools |
error_retry_count | 2 times | Addresses occasional temporary fluctuations in financial interfaces, reduces invocation failure rates |
The parameter values provided on this page are common starting points for configuration work. Actual values are influenced by material form, data volume and business rules, and require targeted analysis for individual cases. It is recommended to conduct tests on independent samples before finalizing settings.
Three Common Mistakes
- When invoking the MCP tool to pull yield data, a 400 status code is returned, and the model runs normally when the tool is not invoked. The cause is that
field_mapping_rulesare not configured, and the request fields passed by the model do not match the fields required by the MCP interface. - When creating a question classification node, an error occurs when running or saving and publishing using the initialized AI model, and normalcy is restored after switching models. The cause is that the context window or field processing logic of the initialized model does not adapt to the structured daily report data structure.
- When invoking the model to generate visual content, only text descriptions are returned, and no pie charts or bar charts are output. The cause is that
response_formatis not configured to a format that supports structured visualization, and the model does not receive clear instructions to generate charts.
How to Confirm Successful Configuration
- View model invocation logs to confirm that trigger timing matches the daily update rhythm, with no duplicate or missed invocation records.
- Check field mapping results to confirm that pulled data fields are fully aligned with configured
field_mapping_rules, with no missing or incorrectly mapped fields. - Verify model output format to confirm that returned content complies with preset
response_formatrequirements, and can be directly used for broadcast scenarios. - Simulate a temporary exception scenario of a financial interface to trigger an invocation, confirm that the
error_retry_countconfiguration takes effect, and normal invocation is restored after retries.
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.