What the data for this category looks like
Data for white goods industry research reports comes primarily from domestic securities firm home appliance industry research institutes, the China Household Electrical Appliances Association, public financial reports of leading listed companies, and third-party market research institutions. Update cycles fall into three categories:
- Monthly updated offline and online channel sales data
- Quarterly updated full-category shipment volume and average price analysis
- Annually updated in-depth industry trend reports, plus real-time special research reports for sudden policy changes or new product launches
Most documents are in PDF format, with some including exportable Excel attachments. The body of each report follows a standard structure: basic report information, overall industry overview, core data for segmented categories, competitive landscape, and future outlook. Core fields include shipment scale (unit: ten thousand units), retail average price (unit: yuan per unit), shipment volume of leading brands, key policy adjustments, and new product technical parameters.
Constraints on multi-turn dialogue and prompting
The data sources for white goods industry research reports are scattered, including public research reports, industry association data, and corporate financial reports. Multi-turn dialogue must first clarify the data source type required by the user. Prompting must limit the recall scope to avoid confusion.
Single research reports contain long text bodies and structured Excel attachments. Systems must support recall and parsing of multi-format content. Prompting must distinguish extraction logic for text and structured data.
Core fields have fixed units. Multi-turn dialogue must standardize unit output. Prompting must require the model to mark corresponding units.
The update frequency of different data sets varies widely. Users often specify time ranges for queries. Prompting must include clear time limit requirements to prevent returning outdated data.
How to configure the settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | The body of a single white goods industry research report often exceeds 5000 characters. Retaining sufficient context supports context association during multi-turn follow-up questions |
UPLOAD_FILE_MAX_SIZE | 50 MB | The total size of a single research report PDF and supporting Excel attachments usually does not exceed 30 MB. Reserves reasonable buffer space |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Parsing research reports with multiple attachments typically takes 60-90 seconds. Prevents timeout truncation of data |
Recall Count | Top 6 entries | Data for white goods segmented categories is scattered. Must recall enough entries to cover multiple dimensions including shipment volume, average price, and sales channels |
Similarity Threshold | 0.72–0.78 | Filters low-relevance pan-industry reports, retains content strongly related to white goods |
Conversation History Retention Rounds | 3–5 rounds | Users often ask consecutive questions about different data points from the same research report. Retaining 3-5 rounds maintains conversation coherence |
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 individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- After uploading Excel-format research report attachments, structured data such as shipment volume and average price cannot be extracted during dialogue. Cause: The Excel parsing switch in FastGPT is not enabled, or the field mapping rules for structured data are not configured.
- When calling the dialogue interface, the returned results do not associate with the user's current specific question. Cause: The
{{query}}variable name is not correctly referenced in the prompt template, so the interface cannot pass the actual content of the user's input. - The debug preview-generated history records cannot be cleaned individually by application or session. Only all workspace data can be cleared. Cause: The specified session cleaning interface is not called, or the unique identifier of the corresponding application is not included in the cleaning request.
How to confirm correct configuration
- Upload a local white goods industry research report, check if the parsed text includes core fields, and verify that upload and parsing parameters are effective.
- Initiate multi-turn follow-up questions. For example, first query "2024 overall white goods shipment scale", then follow up with "relevant data for the washing machine category". Check if the model can associate the time range from the previous round, and verify that the context configuration is reasonable.
- Adjust the
similarity thresholdand initiate the same query. Observe changes in the relevance of recall results, and confirm that the threshold configuration meets business requirements. - Call the history record cleaning interface, specify the unique identifier of the current application, check if the debug preview session is successfully cleared, and verify that the cleaning rules are correct.
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.