What the Data for This Use Case Looks Like
Research report data sources include internal operating analysis documents archived by group subsidiaries, and segmented research reports from cross-sector industry cooperation institutions. Update cadences fall into three categories: subsidiary monthly operating reports updated monthly, group comprehensive sector reports updated quarterly, and external industry reports synchronized based on their release timeliness. Document structures include business sector classification, core operating data, subsidiary linkage analysis, and risk warning modules. Fields include sector attribution identifier, operating data value, associated entity name, risk level identifier, with units such as ten thousand yuan, person-times and other specific measurement values.
Constraints Imposed by These Characteristics on Model Access and Configuration
Multi-source data access for cross-sector research reports requires configuring multi-channel data validation rules to avoid field format conflicts across different sources. Research reports with different update cadences require differentiated scheduled synchronization task parameters to adapt to monthly and quarterly update cycles. The complex document structure with multiple modules requires configuring appropriate chunk parsing granularity to avoid information loss caused by long text truncation. Fields containing associated entities and risk levels require configuring entity recognition recall thresholds to ensure accurate extraction of core associated information.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
multi_source_data_validate | Enabled, validate via sector field mapping | Adapts to field differences across multiple sources and sectors for research reports, unifies data formats |
sync_task_cron | Monthly tasks set to 0 0 2 * *, quarterly tasks set to 0 0 2 1 */3 | Matches the monthly and quarterly update cadences of research reports, ensures data synchronization timeliness |
chunk_max_length | 800–1200 characters | Adapts to the multi-module structure of cross-sector research reports, avoids core information loss from long text truncation |
entity_recall_top_k | Top 3–5 entries | Ensures core entities such as associated entities and risk levels are fully recalled |
custom_request_address | Configure a dedicated address per model service provider documentation | Distinguishes between custom request addresses and proxy addresses, adapts to multi-model access requirements |
hide_default_system_intro | Enabled | Removes built-in system identification content from responses, complies with business output specifications |
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 targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Errors
- When testing a workflow, the response always ends with the text "FastGPT is a large language model (LLM)-based knowledge base question answering system". Cause:
hide_default_system_introis not enabled, so default system initialization content is retained. - After accessing multi-source research reports, retrieval failures occur due to field mismatches. Cause:
multi_source_data_validatevalidation is not enabled, and field mapping rules for different sector reports are not unified. - Frequent 504 timeout errors are returned when calling the large language model. Cause: The
request_timeoutparameter value is not adjusted based on the parsing requirements of long research report documents, and the timeout threshold is set too low.
How to Confirm Configurations Are Successfully Applied
- The model configuration page can be accessed to verify the configuration differences between
custom_request_addressand the proxy address, and clarify the access scenario distinction between the two. - A research report retrieval test can be triggered, and the response content checked to confirm that the default system identification text has been removed.
- A cross-sector research report can be imported, and the parsed chunked content reviewed to confirm that the chunk length falls within the preset range.
- The scheduled synchronization task can be run, and verification performed to confirm that research reports with different update cadences have completed synchronization per the 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.