What this type of data looks like
Data sources include oil and petrochemical industry financing disclosure platforms, public announcements from domestic and overseas exchanges, and specialized refining and chemical sector data from industry information terminals. Updates occur within 1 hour after daily market close for newly added refining and chemical financing information. Monthly summary updates cover the full month’s financing ledger. Each document centers on a single financing transaction. Fields include financing subject, financing amount, financing term, fund usage, disclosure date, and production capacity scale of associated refining and chemical projects. Financing amount is measured in ten thousand RMB. Financing term is measured in days. Refining and chemical production capacity is measured in ten thousand tons per year.
How these data characteristics impose constraints on model access and configuration
The daily high-frequency update requirement means sync frequency must match the data update cycle. This avoids data lag from overly long sync intervals, or triggering interface rate limits from overly frequent requests. Fields with specialized industry terminology and specific units require the model to accurately identify and distinguish units such as refining and chemical production capacity and financing amount. This prevents parsing errors. The structure where a single financing transaction corresponds to one document requires the retrieval step to focus on relevance to individual financing transactions. This avoids redundant retrieval of entire daily reports. Information on technical transformation or stock preparation for associated refining and chemical projects requires the model to match industry-specific terminology. Additional custom term recognition rules must be configured to improve extraction accuracy.
Configuration Parameters and Recommended Values
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
syncInterval | 3600 seconds | Matches the daily update schedule of refining and chemical financing reports, which updates within 1 hour after market close. Avoids data lag or interface rate limits |
chunkSize | 800–1200 characters | Adapts to the length of individual financing report entries, prevents text splitting from breaking field integrity |
retrievalTopK | Top 3 entries | Aligns with the low volume of financing entries in a single daily report, reduces irrelevant retrieval results |
retrievalThreshold | 0.75–0.85 | Accurately matches financing fields related to refining and chemical projects, filters low-relevance retrieval results |
enableCustomTerm | Enabled | Recognizes specialized terms such as refining and chemical plant names and capacity units, improves field extraction accuracy |
apiTimeout | 600 seconds | Adapts to potentially long response times for data pulls from industry information terminals |
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 case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- A
lookup spark-api.xf-yun.com i/o timeouterror occurs when calling an external model. The cause is failure to configure a sufficiently longapiTimeoutparameter for industry data pulling, leading to interface response timeout. - Two similar queries return identical results, and specialized refining and chemical project fields are not correctly identified. The cause is failure to enable the
enableCustomTermconfiguration and specify specialized term extraction rules, so the model cannot distinguish between general financing and specialized refining and chemical financing. - The
apiTokenconfiguration item cannot be found when configuring third-party data source access in the cloud space, leading to authentication failure. The cause is failure to switch to custom API access mode. The default cloud space data source link does not allow independent token configuration.
How to Confirm Successful Configuration
- Manually trigger a data synchronization task, verify that the number of pulled data entries in the synchronization log matches the number of daily refining and chemical financing entries disclosed by the industry.
- Submit a query containing refining and chemical plant terms, verify that specialized fields such as financing amount and fund usage are accurately extracted in the returned results.
- View the model call log, confirm that there are no errors such as timeout, authentication failure, or field parsing errors.
- Test two queries with similar structures, verify that the field extraction logic of the returned results conforms to preset rules.
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.