What the data for this category looks like
Data sources for professional services investment research knowledge bases include public industry research reports, periodic announcements of listed companies, regulatory policy documents, and statistical data from industry associations. Update rhythms vary significantly: listed company announcements are released in real time, industry research reports are updated weekly or daily, and regulatory documents are released irregularly.
Document structures include long-text analysis paragraphs, structured financial indicator tables, and risk reminder modules. Fields cover earnings per share, price-to-earnings ratio, industry prosperity index, and similar metrics. Units include yuan, percentage, times, and similar units.
What constraints do these characteristics impose on model integration and configuration
The high proportion of long texts, numerous structured fields, large differences in update frequencies, and strong timeliness requirements for some documents impose multiple constraints on model integration and configuration. Long-text research reports and announcements require adaptation to large context windows to avoid truncation of key information. Structured fields require corresponding parsing configurations to ensure accurate field recognition. For differences in update cycles across data sources, differentiated synchronization trigger rules need to be configured. The strong timeliness requirement for regulatory documents requires shortening the cache cycle and increasing recall priority.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | The average length of a single investment research report can reach tens of thousands of characters. This range reserves sufficient context to accommodate segmented data and query instructions, avoiding overflow of key information |
chunkSize | 1000–1500 characters | Investment research documents include long paragraphs and structured tables. This range balances segmentation integrity and recall accuracy, reducing information fragmentation across segments |
syncInterval | 3600 seconds (public research reports), 60 seconds (listed company announcements) | Update frequencies vary significantly across different data sources. Matching the corresponding cycles balances data timeliness and synchronization resource usage |
structuredParseEnable | Enabled | Investment research data contains a large number of structured fields such as financial indicators and industry indices. Enabling this setting improves the accuracy of field recognition and precise recall |
recallThreshold | 0.75–0.85 | Investment research queries require rigorous matching of core data. This threshold filters low-relevance recall results to ensure the reference value of investment research conclusions |
streamResponseEnable | Enabled | Long document processing requires returning results incrementally, improving feedback efficiency during interaction |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material formats, 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
- Issue: A call failure is returned when connecting the
qwq-plusmodel viaoneapi. Cause: Some access gateways do not adapt to the exclusive interface parameter format of this model, causing request verification to fail. - Issue: Model call timeout exceptions occur after updating the
aiproxyandsandboxcomponents. Cause: The local configuration file was not updated synchronously according to the version instructions, resulting in incompatibility between component versions and configuration parameters. - Issue: When adding the
gpt-4.1-miniorqwen3models, the interface prompts "Model stream response is empty". Cause: The streaming response configuration was not enabled correctly, or the access gateway did not correctly forward streaming data fragments.
How to confirm successful configuration
- Initiate a query containing structured financial indicators, and check whether the returned results accurately extract the corresponding fields and values.
- View the data synchronization task logs to confirm that the synchronization intervals for public research reports and listed company announcements match the preset configurations.
- Trigger a model call test to confirm that the streaming response can return results in normal segments without empty response errors.
- Verify the tool call logic to confirm that the model can independently choose whether to call preset tools based on query requirements.
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.