What the data for this category looks like
The data sources for multi-financial research reports primarily include licensed securities firm research institute non-bank financial group reports, public databases of industry self-regulatory organizations, and official disclosure documents of non-bank financial institutions. Update timing adjusts based on industry events, regulatory policies, and institutional earnings report cycles, with no fixed schedule. Core reports are concentrated during earnings seasons. Document structures typically include abstracts, industry fundamental data tables, institutional business breakdowns, and risk warning modules. Fields include report ID, release date, covered institutions, core business indicators, and some indicators have exclusive measurement units.
Constraints on workflow orchestration from these characteristics
The multi-source and scattered nature of multi-financial research reports requires workflows to include multiple data source access nodes and unified field mapping rules. This prevents retrieval deviations caused by differences in data formats across report sources. The non-fixed update cycle requires workflows to support a combination of event-triggered and scheduled-triggered modes. This adapts to both sudden report releases and routine update scenarios. The large number of structured tables in documents requires a pre-configured table parsing node in the workflow. This extracts structured fields for precise matching and improves retrieval accuracy. The exclusive measurement units attached to fields require a standardized field mapping node in the workflow. This unifies indicator naming and measurement rules for reports from different sources, avoiding unit confusion during retrieval.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_TABLE_ENABLE | Enabled | Multi-financial research reports contain a large number of structured business data tables. Enabling this setting extracts structured fields to meet precise matching retrieval requirements |
rag_chunk_size | 800-1200 characters | Adapts to the length of core viewpoints in single paragraphs of research reports, avoiding context breaks caused by excessive splitting |
rag_chunk_overlap | 150-200 characters | Retains logical connections across paragraphs, preventing loss of context for core indicators |
TOOL_MAX_CONCURRENCY | 1 | Prevents retrieval order chaos caused by concurrent tool calls, and adapts to the serial call logic of research report retrieval |
RECALL_TOP_K | Top 6-8 entries | Covers scattered core business data in multi-financial research reports, while controlling redundancy of returned results |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Adapts to parsing time for long research report documents, preventing parsing failures caused by timeouts |
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 individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: The workflow returns empty results or a
500 Internal Server Errorwith no clear exception prompt. Cause: No exception capture node is configured to handle cases where the large language model returns empty values or tool calls throw errors. Raw exceptions are directly passed to the frontend. - Symptom: Multiple tool executions trigger simultaneously in the tool call node, leading to cross-mixed returned results. Cause: The
TOOL_MAX_CONCURRENCYparameter is not set to 1, allowing concurrent tool calls. This does not match the serial call logic required for research report retrieval. - Symptom: When a designated workflow is triggered, another pre-configured workflow actually executes. Cause: No unique trigger identifier is set for each workflow, leading to matching deviations in trigger rules.
How to confirm correct configuration
- Upload a multi-financial industry research report document, trigger workflow execution, and check if the parsing node successfully extracts structured fields from the table.
- Simulate scenarios where the large language model returns empty values or tool calls throw errors, and check if the preset exception handling process triggers and returns clear prompt information.
- Configure multiple independent research report retrieval workflows, each using a different trigger identifier. Verify that only the corresponding workflow executes when the designated identifier is triggered.
- Adjust the
RECALL_TOP_Kparameter, retrieve a specified keyword, and check that the number of returned results matches the preset value range.
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.