What data does this category consist of?
Multi-financial research report data comes primarily from publicly disclosed documents of licensed securities research institutions, industry self-regulatory organizations, and compliant research meeting minutes. Update timing syncs in real time with report release. Some high-frequency tracked categories support same-day updates.
Document structure follows a fixed format: title, publishing entity, publishing date, investment rating, target price, core logic section, and risk warning items. Fields include exclusive financial indicators: rating level (no unit), target price (RMB yuan), position size (100 million yuan), and number of research meeting participating institutions (unit: number of institutions).
What constraints do these characteristics impose on the knowledge base retrieval and recall link?
The compliance requirement for data sources of multi-financial research reports mandates that the recall link prioritize content published by licensed institutions, and filter invalid information from non-compliant sources. High-frequency update rhythms require incremental synchronization logic to avoid resource consumption from full scans. The fixed professional field structure requires field-level recall rules for exclusive indicators such as investment ratings and target prices, to improve matching accuracy. Long single report length requires split paragraph lengths to fit model context windows, preventing truncation of key logic. Some research reports contain cross-category associated data. Recall processes must retain contextual associations for linked fields, to ensure the binding relationship between target price and rating remains intact.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall count | Top 10-15 entries | Multi-financial research reports have high professional complexity and long length. Excessive recall will exceed model context windows, leading to truncation of critical information |
segment length | 800-1200 characters | Core logic of single research reports concentrates in medium-to-long paragraphs. This length preserves complete logical units, avoiding damage to professional expressions during splitting |
similarity threshold | 0.75-0.85 | The financial field has many professional terms. A higher matching threshold filters irrelevant content, ensuring recalled content aligns closely with query requirements |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Single research report documents can reach thousands of words. Parsing requires sufficient time for text splitting and field extraction |
incremental sync switch | Enabled | Multi-financial research reports have high update frequencies. Incremental synchronization reduces storage and computing resource consumption from full scans |
reorder return count | Top 5-8 entries | Retains the most relevant core research report content, avoiding interference from excessive redundant information for model answer generation |
The parameter values provided on this page are conventional recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific scenarios require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: The dynamic knowledge base variable
knowledgeSearchis configured with passed parameters, but the generated answer does not display cited documents, or cited fields are empty. Cause: Contextual association between the knowledge base retrieval node and large model call node is not bound in the workflow, so retrieval results are not passed to the generation link. - Phenomenon: Only text content is recalled for uploaded PDF and table documents in the knowledge base, while table and chart information is not cited. Cause: Table extraction and vectorization configuration in document parsing is not enabled, so only plain text content is vectorized.
- Phenomenon: Retrieval results include conflicting investment ratings and target prices across multiple research reports, with no automatic marking of conflicting items. Cause: No conflict detection rules are configured. Content is only recalled by similarity, without verifying field consistency across recall results.
How to Verify Configuration Setup
- Upload a test multi-financial research report. Check if parsed fields include exclusive indicators such as investment rating and target price, to confirm parsing configuration is active.
- Initiate a query targeting the core logic of the research report. Check if the reference list of returned results includes the uploaded test document, to confirm retrieval and association configuration is active.
- Adjust the value of
similarity threshold. Observe changes in the number of recall results, to confirm threshold configuration impacts recall logic as expected. - Enable the incremental sync switch. Upload a new research report, wait for synchronization to complete, then initiate a query to confirm new content can be normally recalled.
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.