What the data for this category looks like
Snack food financial report data draws from public periodic reports disclosed by domestic and overseas stock exchanges, plus official operating briefings released by companies. Release schedules follow fixed rules: annual reports publish within four months after the accounting year ends, semi-annual reports within two months after the first half of the year ends, and quarterly reports within one month after the quarter ends. Document structures include core operating data sections, detailed category-specific revenue breakdowns, channel sales data, cost composition, cash flow and liability status. Fields include revenue amount, sales volume, unit cost, channel revenue amount, and similar metrics. Common units are RMB yuan, tons, pieces, and similar units.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Scattered category-specific revenue details and channel data demand precise multi-turn dialogue guidance to prompt users to clarify sub-categories and channel types, avoiding result deviations from vague queries. Long financial report document lengths require limiting per-turn dialogue context length, preventing content loss from exceeding model context windows. Varied field naming across report cycles requires adding field alignment guidance rules in prompts to unify data standards. Frequent need to compare multiple financial reports requires retaining valid historical query information in dialogue context, avoiding repeated basic parameter confirmation.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 tokens | Single snack food financial report documents have high total token counts after chunking. Sufficient context retains multi-turn dialogue historical query and analysis logic |
chunkSize | 1000–1500 characters | Category-specific revenue and channel data span multiple paragraphs. Moderate chunk sizes preserve correlation between related fields |
similarityTopK | Top 8–12 results | Financial reports have many fields. Sufficient relevant document fragment recall covers core analysis data |
rerankTopN | Top 4–6 results | Filters redundant chunked financial report content, focusing on core data directly tied to current queries |
fileParseChunkOverlap | 100–200 characters | Category-specific revenue data may span multiple chunks. Overlapping chunks preserve context coherence |
maxResponseLength | 4000–6000 tokens | Financial report analysis reports have significant length. Prevents forced truncation of long text outputs |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three common configuration errors
- Phenomenon: Knowledge base calls return truncated answers when accessing financial report data, with only partial analysis content included. Cause: The
maxResponseLengthparameter is unadjusted, or model context window configuration is insufficient, leading to forced long text output truncation. - Phenomenon: AI-generated content retains
<think>tag-wrapped thinking material after workflow runs. Cause: No thinking tag removal rule is configured in the prompt template, or the code running node is not correctly bound to the AI dialogue node output field. - Phenomenon: Analysis results show mixed cross-cycle data after switching financial report cycles during multi-turn dialogue. Cause: No requirement to specify current analysis report type and cycle before each query is clearly stated in the prompt, leading to redundant context data interfering with current analysis logic.
How to verify correct configuration
- Upload a single annual snack food financial report document, trigger multi-turn dialogue, verify output content covers required analysis dimensions including category-specific revenue and channel data, adjust the prompt template until requirements are met.
- Test cross-cycle queries across different report cycles, verify context retains valid historical query information, adjust the
maxContextparameter value range. - Run a workflow with a code node, verify AI-generated analysis content contains no
<think>tags, confirming prompt and code node configurations are effective. - Upload multiple financial report documents from different cycles, test recalled result relevance and count, adjust
similarityTopKandrerankTopNvalues.
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.