What Data Looks Like for This Category
Black home appliances financial report data mainly comes from official periodic reports disclosed by listed companies and public retail monitoring datasets. Quarterly data updates once per quarter, annual data updates once per year, and retail-side detailed data updates daily. A single financial report document usually includes main business category breakdowns, shipment volume, channel revenue, R&D investment, inventory turnover and other content. Core fields include revenue amount (unit: CNY), shipment volume (unit: 10,000 units), R&D investment amount (unit: 10,000 CNY), inventory turnover days (unit: days), with no additional standardized supplementary fields.
Constraints on Tool Calling and Plugins
Mixed calls across multiple data sources require configuring multiple sets of authentication parameters to adapt to different sources. High-frequency updated retail data requires tool calls to support scheduled triggers on a daily or quarterly basis. Detailed category-specific data structures require tools to accurately match specific tags to extract fields and avoid invalid data mixing. Long document length requires tool calls to adapt to long-text parsing and segment processing, while also considering call frequency limits to avoid rate limiting.
Configuration Settings
| Config Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Black home appliances financial report documents usually include multi-category detailed data, leading to longer parsing times |
RECALL_CHUNK_SIZE | 800–1200 characters | Category-specific revenue fields in financial reports are scattered. Appropriate segment length preserves field association relationships |
API_REQUEST_INTERVAL | 10–15 seconds | Some public data sources have call rate limits, to avoid triggering rate limiting |
FIELD_EXTRACT_RULE | Match by the "main business category breakdown" tag | Core data of black home appliances financial reports is concentrated under this tag. Accurate matching reduces invalid data |
HTTP_REQUEST_BODY_ENCODING | UTF-8 | Financial report data includes Chinese and special unit symbols, ensuring correct request encoding |
SESSION_KEEP_ALIVE | Enabled | The same financial report analysis task requires consistent session to avoid context loss |
The parameter values provided on this page are common 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
- Character misalignment occurs in text passed during tool calling, such as the term "share" being incorrectly transcribed as "split". No pre-verification and error correction rules for input text are configured, or the used transcription plugin is not optimized for Chinese professional text. Black home appliances financial reports have many professional terms, which easily cause recognition deviations.
- Multiple API call return results cannot be associated with the same analysis task. Session keep-alive configuration is not enabled, so each call generates an independent context, and previously extracted financial report data results cannot be reused.
- HTTP request components cannot receive numeric parameters passed by plugins, returning
400 Bad Requestor "Request failed with" errors. No parameter type mapping for the request body is configured, so numeric parameters are automatically converted to string format, which does not meet the parameter requirements of the target API.
How to Verify Proper Configuration
- Upload a local black home appliances financial report PDF, trigger tool calling, and check if the parsed segments include core fields such as category-by-category revenue and channel data. Confirm that the segment length configuration meets expectations.
- Call the configured API interface, initiate three identical tasks consecutively, and check if the return results are associated with the same session context. Confirm that the session keep-alive configuration takes effect.
- Construct a test request containing numeric parameters, check if the parameter types of the HTTP request match the requirements of the target API, and confirm that the parameter mapping configuration is correct.
- Simulate high-frequency calls, check if rate limiting errors are triggered, and confirm that the request interval configuration complies with data source limits.
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.