What Data for This Category Looks Like
White goods investment research data comes from brand official specification documents, industry association monthly reports, e-commerce platform sales monitoring data, upstream raw material price APIs, and offline terminal feedback data. Update rhythms vary significantly:
- New product launch information is updated irregularly
- Monthly sales data is organized weekly by third-party institutions
- Raw material prices are updated in real time
- Industry policy documents are updated irregularly alongside national standard adjustments
Document structures include fields such as product SKU, energy efficiency rating, dimensions (millimeters), power (watts), selling price (CNY), supply chain cost, and regional sales share. Some documents consist of unstructured user reviews and complaint content.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Multi-source data with inconsistent update frequencies requires clear differentiation between static knowledge bases and real-time tool call triggers during multi-turn dialogue. This prevents confusion between historical data and latest updates.
The scattered multi-field document structure requires prompts to precisely specify target fields and units for extraction. This stops the model from missing key investment research information.
Data with regional and model differences requires active prompting of users for unspecified parameters during multi-turn dialogue. This ensures accurate responses.
Unstructured user review data requires semantic clustering before being added to the conversation context. This avoids invalid information disrupting dialogue logic.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Single white goods investment research documents often exceed 2000 characters. This range accommodates multi-turn dialogue context and retrieved cross-dimensional data |
toolCallInterval | 45–60 seconds | API call frequency for raw material prices and online sales data is restricted. This range avoids triggering rate limits |
recallTopK | Top 8–12 entries | Investment research covers multiple dimensions including product specifications, supply chain, and market feedback. Too few retrieved entries miss critical data, while too many dilute valid context information |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Large monthly supply chain reports and full product line manuals take longer to parse. The default 120 seconds is insufficient for complete splitting |
promptTemplate | Follow the order: "first verify product model and region, then extract data per specified fields, finally respond to investment research questions" | White goods data fields are scattered and have regional differences. This structure guides the model to actively request missing information during multi-turn dialogue |
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 individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Issue: Tool call results do not appear in the dialogue interface, and only display after re-entering the session. Cause: The expiration time for
toolCallCacheis not configured, leading to delayed cache refresh. Latest tool results cannot be pulled in real time during multi-turn dialogue. - Issue: The iframe-embedded dialogue interface shows English copy. Cause:
localeis not set tozh-CNin initialization parameters. The default English language pack loads instead. - Issue: Frequent Request Time errors occur when deploying fastgpt-v4.9.0 locally with ollama. Cause: The
ollamaRequestTimeoutparameter is not adjusted to above 180 seconds. Long document parsing and multi-turn retrieval for white goods investment research increase request latency. The default timeout period is insufficient.
How to Confirm Correct Configuration
- Initiate an investment research question with multi-turn follow-ups. For example, first ask about the energy efficiency rating of a specific white goods model, then follow up with monthly sales data for its region. Verify that the model associates product and region information across turns.
- Trigger a tool call request. Verify that the dialogue interface displays raw material price or sales data returned by the tool in real time, with no delayed loading.
- Upload a full product line manual. Verify that parsed text splitting matches the
PARSE_FILE_TIMEOUT_SECONDSsetting, with no parsing interruption errors. - Check initialization parameters in the iframe embedding code. Confirm that the
localefield is configured, and the interface has no English copy.
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.