What the data looks like for this use case
Data sources include internal group accounting systems, operating report data from subsidiary entities, and third-party financial market APIs. Data is compiled and generated daily after market close. All business segments must undergo full verification before release. Document structure uses business segments as core split units. Each daily report includes structured fields: segment identifier, daily profit and loss, cumulative revenue scale, held asset net value, and more. All fields use legal tender units. Short notes for daily market movements for each segment are included.
Constraints for form and interaction workflows
Since data comes from multiple sources and is split by business segment, form interactions must support field mapping configuration across multiple data sources. This ensures unified display of segment data from different origins. Data updates follow a fixed schedule. The interaction flow must restrict the valid time range for query triggers. This prevents users from accessing unvalidated incomplete data. Detailed fields for multiple segments increase total information volume. The form must support segment filtering and custom field display to avoid interface overload. Data has two tiers: group-wide and individual sub-segments. Interactions must support tiered expansion to view details. This allows users to access information at their required granularity.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recallCount | Top 8-12 entries | Daily reports cover multiple business segment market data, must cover enough segments to meet regular query needs and avoid missing core information |
maxContext | 8000-12000 characters | Detailed data for group-level daily reports is voluminous. Must support complete segment fields and note content, to align with large model context limits |
SIMILARITY_THRESHOLD | 0.72-0.80 | Must distinguish revenue data across different business segments, prevent low-relevance segment information from appearing in query results, and improve information accuracy |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Daily reports typically include multiple subsidiary detailed statements. Total parsing time is long, so sufficient document processing time must be reserved |
UPLOAD_FILE_MAX_SIZE | 500 MB | Daily report documents may include multiple attachments and detailed tables. Total file size is large, so upload support for large files is required |
SCHEDULER_CRON | 0 18 * | Domestic financial markets close at 15:00 daily. Reserve 3 hours for data compilation and verification. Set the automatic update task to trigger at 18:00 daily |
The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Each scenario requires separate analysis. It is recommended to test with your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: A 400 error occurs when inputting specific segment values while calling
gpt-4o-mini. Functionality returns to normal after switching to another model. Cause: No context truncation rule is configured for the large language model, causing the request body to exceed the token limit supported by the model. - Phenomenon: The broadcast content returned after voice input does not match the daily report data, and irrelevant segment revenue information is included. Cause: No mapping verification is performed between voice-recognized text and preset business segment fields, leading to semantic confusion when passed directly to the large language model.
- Phenomenon: After deploying a local LLM, interaction return results are unrelated to the knowledge base content. Performance is normal when using local inference tools. Cause: No model context recall parameters are configured, causing knowledge base content to not be correctly loaded into the model inference workflow.
How to Verify Correct Configuration
- Upload a test daily report document. Check that parsed fields include preset items such as business segment identifier and daily profit and loss amount, with no parsing error prompts.
- Initiate a query containing a specific business segment name. Verify that returned results only include revenue data for the corresponding segment, with no irrelevant segment information included.
- Set a scheduled trigger task. Check that automatically generated broadcast content includes the latest update timestamp after daily market close, confirming data timeliness.
- Test the voice input function. Confirm that recognized text correctly matches preset segment and value fields, with no semantic deviation or field misalignment.
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.