What the data for this category looks like
Research data for investment platforms comes from multiple sources: public announcements of listed companies, brokerage research reports, macroeconomic indicators, industry white papers, and real-time trading quotes. Update cycles vary significantly: announcements and market data are updated near real time. Brokerage research reports follow institutional release schedules for updates. Macroeconomic data is released on fixed cycles. Documents include standardized fields such as stock code, issuing entity, release date, rating target price, valuation metrics, and more. Some documents include charts and attachments, and individual research reports can be lengthy.
What constraints these characteristics impose on model integration and configuration
The multi-source heterogeneous nature of research data requires model integration to adapt to parsing rules for different formats such as announcements, research reports, and market data. Long documents and frequently updated data require adjustments to segment and incremental update configurations. The presence of specialized fields and units means prompt engineering must clearly define field definitions and recognition rules during configuration, to prevent the model from confusing professional terms. Multi-dimensional research analysis needs also require recall and context configurations to balance information breadth and accuracy, to avoid missing core logic or introducing irrelevant content.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | Meets context carrying requirements for single long research reports or multiple announcements, to avoid truncating core logic |
chunkSize | 1000–1500 characters | Balances research report paragraph integrity and recall accuracy, to avoid splitting that breaks professional logical chains |
similarityThreshold | 0.72–0.85 | Filters low-relevance industry or individual stock information, to meet the high professional standards required for research data |
recallCount | Top 8–12 results | Covers multi-dimensional research information, to meet the need for multi-source data support in individual stock or industry analysis |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Reserves sufficient time to parse large research reports or batch-uploaded document collections |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Meets business requirements for batch uploading industry white papers and historical research report collections |
The parameter values provided on this page are standard starting points for configuration setup. Actual values depend on material format, data volume, and business rules. Each scenario requires targeted analysis. It is recommended to test against your own samples before finalizing settings.
Three common mistakes
- A
504 Gateway Timeouterror occurs during model calls, and output content is interrupted mid-process. Cause: Research documents are segmented too long, or too many results are recalled, exceeding the model's context carrying limit, leading to request timeout and interruption. - After uploading an audio file, no transcribed text is generated, and the interface displays "Parsing failed". Cause: Voice-to-text model integration parameters are not configured correctly, or the audio file parsing switch is not enabled.
- A version incompatibility error occurs after integrating the mineru model, and the interface displays "Module failed to load". Cause: The specific compatible version officially supported by mineru is not installed; installing the latest version directly leads to interface mismatch.
How to confirm configuration is complete
- Upload a single lengthy research document, check that the parsed segments retain core logic with no obvious truncation.
- Enter a professional research query, verify that the relevance and quantity of recall results meet business requirements.
- Trigger a model call, observe that output is complete with no mid-process interruptions.
- Upload audio or visual files, verify that parsed content can be processed normally by the model.
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.