What the data for this use case looks like
Securities research report data comes from licensed financial information service platforms and official research release channels of securities firms. Updates align with research report publication on trading days, with no updates on non-trading days. Document formats are primarily PDF, with partial support for web page parsing. Document structures include fixed fields: research report title, issuing entity, release time, investment rating, target price, core argumentation section, risk reminder module, and some reports include embedded financial data tables. The unit for the target price field is Renminbi yuan. Investment ratings use standardized enumerated text, with minor variations in field formatting across different data sources.
Constraints imposed on workflow orchestration by these characteristics
Configure workflows with multiple parallel pull nodes and a unified format cleaning step to handle multi-source research report data, adapting to field differences across data sources. Set a scheduled trigger node for the workflow to support real-time updates on trading days: execute pull tasks on a trading day cycle and automatically skip runs on non-trading days. Configure the parsing node with a structured parsing plugin to handle embedded tables and long-form text in documents, supporting table field extraction and long-text segmentation. Add rules at the data validation node to enforce requirements for specific field units and enumerations, preventing cross-currency or non-standardized rating content from flowing into subsequent stages. Add a validation node at the end of the workflow to filter unauthorized research report content to meet compliance requirements.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Securities research report PDFs typically include multi-page tables and long text, with longer parsing times than general documents |
maxContext | 8000–12000 characters | The core argumentation section of research reports has lengthy content, requiring sufficient context for large model reference |
Recall Count | Top 8–12 results | There are many documents on the same topic for securities research reports, requiring screening of highly relevant results |
Similarity Threshold | 0.75–0.85 | Filter low-relevance generic financial content, retaining research report fragments that strongly match user queries |
Reranked Return Count | Top 3–5 results | Focus on the most core research report content, preventing large models from receiving excessive redundant information |
HTTP_REQUEST_TIMEOUT | 60 seconds | API connections to financial information service providers carry network delay risks, requiring sufficient request time |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Mistakes
- Symptom: Workflow execution returns empty results, and logs show the file parsing node failed to load the target document. Cause: The research report URL returned by the external data source was not configured as a file link variable for the workflow, so the parsing node could not obtain the specified document.
- Symptom: Calling an external script interface returns a 400 status code, and the interface prompt indicates missing required parameters. Cause: The body parameter name of the HTTP request node does not match the field name required by the interface, using a custom name instead of the parameter name defined by the interface.
- Symptom: The AI chat node only returns preset prompt content and does not associate the user's initial question. Cause: In workflow version 4.6.9, after adding a judge node, the upstream user question variable was not bound to the input parameters of the AI chat node, causing the context link to break.
How to Confirm Proper Configuration
- Manually trigger the workflow, enter a query targeting a specific research report, and review the input and output parameters of each node in the workflow log to confirm the file link variable is correctly bound to the parsing node.
- Call the connected external data source interface, manually verify that the returned research report data format matches the parsing rules configured in the workflow, and confirm no abnormalities in field extraction.
- Adjust the similarity threshold value, compare recall results across different thresholds, and confirm the screening logic meets business requirements.
- Simulate triggering the scheduled workflow on a non-trading day, check whether the node skips the data pull step, and confirm the update rhythm configuration is correct.
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.