What the Data for This Category Looks Like
Financial report data for condiment enterprises primarily comes from annual and quarterly reports publicly disclosed by domestic and overseas stock exchanges, as well as operating briefings officially released by enterprises. Data updates follow a fixed schedule: regular reports are released quarterly and annually, while operating briefings are mostly updated alongside monthly operating milestones. Document structures typically include dedicated sections such as consolidated financial statements, product-specific revenue breakdowns, and cost structures. Core fields include total revenue, category-specific revenue, gross margin, raw material procurement amounts, and more. Units are mostly Renminbi yuan or ten thousand yuan.
What Constraints Do These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
The fixed update schedule for condiment financial reports requires multi-turn dialogue systems to support verifying data timeliness according to the financial report release cycle, to avoid using outdated information. The dedicated disclosure of product-specific revenue requires prompts to accurately target the corresponding fields, to avoid confusing total revenue and segmented category revenue data. The multi-section, long-text document structure requires multi-turn dialogue to gradually guide users to focus on specific modules, while also limiting the context cache length to prevent overflow. The variety of units requires the system to automatically unify output units, to avoid user confusion between yuan and ten thousand yuan.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Core section text of a single-quarter condiment financial report is approximately 5000 characters; reserve cache to handle context calls for multi-turn follow-up questions |
Recall count | Top 6 entries | Key information such as product-specific revenue and costs in condiment financial reports is scattered across different paragraphs; sufficient matching fragments must be retrieved |
Similarity threshold | 0.72–0.78 | Distinguish semantic differences between total revenue and product-specific revenue; filter irrelevant industry macro data fragments |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing a single annual financial report XLSX or PDF file takes a long time; avoid parsing failure due to timeout |
UPLOAD_FILE_MAX_SIZE | 20 MB | Complete annual financial report documents typically do not exceed 15 MB; set a reasonable upload upper limit to accommodate special format files |
Chunk size | 1000 characters | Adapt to segment parsing of long financial report text; retain complete semantics of product-specific revenue paragraphs |
The parameter values provided on this page are conventional recommendations used as a starting point 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 settings.
Three Common Mistakes
- Symptom: When calling the file upload recognition interface, XLSX-format financial reports fail to trigger AI dialogue. Cause: The knowledge base parsing switch for XLSX files is not enabled, or the parsing timeout parameter is set too low, resulting in incomplete parsing.
- Symptom: The model prompts that the knowledge base is empty during dialogue, but all text content can be viewed on the knowledge base page. Cause: The uploaded financial report files are not associated with the current conversation application, or the recall threshold is set too high, resulting in no matching fragments being retrieved.
- Symptom: The trigger timing of content annotations in the workflow does not meet expectations, executing early or not after the stream reply is completed. Cause: The trigger condition of the AI node in the workflow is not configured correctly, the annotation is incorrectly bound to a single-round reply, and not bound to the stream completion event.
How to Confirm Successful Configuration
- Upload a single quarterly financial report file, check the knowledge base parsing log, and confirm the file parsing status is successful.
- Initiate the first round of dialogue by asking "Total revenue for this quarter", verify that the unit used in the model's reply matches the financial report document.
- Initiate multi-turn follow-up questions, first ask "Revenue share of soy sauce business", then "Raw material cost changes", confirm that the model can associate context to extract corresponding segmented category data.
- Test passing a compliant image URL, confirm that the model can correctly recognize and associate it with the conversation context.
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.