What the data for this category looks like
Kitchen and bathroom appliance financial report data mainly comes from periodic reports disclosed by domestic and overseas stock exchanges, and publicly available industry monitoring data. Updates follow quarterly and annual core cycles, with some channel sales data updated monthly. Document structures include modules such as segment operating data, cost composition, R&D investment, and channel proportion. Fields cover single-category revenue, units sold, gross margin, and more. Common units are RMB 10,000 yuan, units sold, and unit selling price yuan per unit.
What constraints these characteristics impose on multi-turn dialogue and prompting
Kitchen and bathroom appliance financial reports have numerous segment data dimensions, fixed update cycles, and different calibers. Multi-turn dialogue must retain context about the category (such as range hoods, integrated stoves) and time range to avoid dimension drift during follow-up questions. Prompts must clearly limit analysis to kitchen and bathroom appliance segment operating data, distinguish caliber differences between quarterly financial reports and monthly channel data, and unify output units to prevent confusion. Initial dialogue prompts should also inform users of disclosed report cycles that can be queried, to avoid requests for undisclosed data.
How to set the configurations
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
maxContext | 8000–12000 characters | The segment operating text of kitchen and bathroom appliance financial reports is lengthy. Multi-turn dialogue needs to retain 3-4 rounds of category, time, and indicator context to avoid context overflow |
RECALL_TOP_K | Top 6–8 entries | Kitchen and bathroom appliance financial reports include multi-dimensional fields such as segment revenue, costs, and channels. Sufficient relevant fragments must be retrieved to cover users' multi-turn follow-up questions |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Kitchen and bathroom appliance financial report Word documents over 10M contain nested tables and segment data, which take longer to parse |
SIMILARITY_THRESHOLD | 0.75–0.85 | Field names in kitchen and bathroom appliance financial reports have high similarity, so low-relevant knowledge base retrieval fragments need to be filtered out |
PROMPT_TEMPLATE | Fixed pre-constraint of "Only analyze kitchen and bathroom appliance segment operating data, distinguish the caliber differences between quarterly financial reports and monthly channel data, and output unified units" | Avoid analysis errors across categories and data calibers, unify output format |
The parameter values provided on this page are common recommended 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 errors
- Phenomenon: When calling a dialogue application via API, conversation logs and returned results include the title content of knowledge base documents. Cause: No metadata shielding rule was configured in the prompt template, leading retrieved knowledge base fragments to include unnecessary document title information.
- Phenomenon: When uploading kitchen and bathroom appliance financial report Word documents larger than 10M, the parsing process takes too long or triggers a timeout error. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted to a duration suitable for large files, or automatic document chunking parsing configuration was not enabled. - Phenomenon: When asking multi-turn questions about revenue data for different kitchen and bathroom appliance categories, the model confuses category and time range from prior rounds, outputting incorrect analysis results. Cause: The
maxContextparameter was not set large enough, causing the context window to overflow and lose key information such as category and time from prior follow-up questions.
How to confirm the configuration is correct
- Initiate a single-round test dialogue, request kitchen and bathroom appliance financial report data for a specified category and time, and verify that the analysis scope of the returned results matches preset constraints.
- Initiate consecutive multi-round follow-up questions, and verify that the model retains key information such as category and time from historical conversations, with no analysis dimension drift.
- Upload a kitchen and bathroom appliance financial report document of conventional size, and verify that the parsing process completes normally without triggering timeout errors.
- Call the API interface to initiate a dialogue, and verify that returned content only includes preset analysis dimensions and no irrelevant metadata.
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.