What the Data for This Category Looks Like
Home goods research report data primarily comes from light manufacturing industry databases, publicly available broker research reports, financial reports of leading home goods brands, and offline retail monitoring agency data. Update cycles include fixed-period quarterly and semi-annual industry overview reports, as well as ad-hoc updates following new product launches, large home goods exhibitions, or industry policy announcements. Document structures typically include modules such as overall industry overview, market performance of segmented categories (e.g., soft furnishings, kitchen appliances, bedding), channel share, competitor dynamics, and policy impacts. Fields include SKU codes, offline store counts, online transaction data, average order value, exhibition holding dates, brand names, and some data includes unit identifiers.
What Constraints Do These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
The multi-source data nature of home goods research reports requires multi-turn dialogue to first clarify the data type the user needs, to avoid confusion between retail monitoring data and broker research report content. The large number of segmented categories requires dialogue to gradually guide users to supplement specific parameters such as category, time range, and channel type, to avoid generic queries returning irrelevant results. The large number of structured tables and specialized terminology in documents requires prompts to clearly specify rules for parsing structured data, to avoid generating vague responses. The ad-hoc updated exhibition and new product data requires dialogue to support users specifying whether they need the latest updates, and to note that such data has no historical comparison information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | First 8 turns of conversation context | Home goods research report queries involve multi-dimensional parameter supplementation. 8 turns of context can cover key information such as category, time, and channel from follow-up questions, avoiding repeated inquiries |
Recall count | Top 12 results | Home goods have a wide range of segmented categories. 12 retrieved results can cover relevant data across different brands and channels, avoiding missing information from segmented scenarios |
Similarity threshold | 0.72–0.78 | Home goods research reports contain a large number of specialized terms. A threshold that is too low will introduce irrelevant content, while a threshold that is too high will miss accurate matched results for segmented categories |
Chunk size | 1000–1200 characters | A single home goods research report contains multiple pages of structured tables and competitor comparison paragraphs. A 1000–1200 character segment length preserves complete logic for individual data groups |
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | A single home goods research report may contain multiple pages of retail monitoring data. 900 seconds allows complete document parsing and vector database import |
Rerank result count | Top 6 results | The top 6 most relevant core data entries are retained after re-ranking, adapting to the need to gradually narrow query scope during multi-turn dialogue |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- File download cannot be triggered via the conversation interface, and returned BLOB data cannot be clicked by users. This occurs when the
DOWNLOAD_BLOB_ENABLEparameter is not set to enabled, and the correct file name and MIME type fields are not bound after the HTTP node. - Supplementary segmented category information from users is lost during multi-turn dialogue, preventing accurate matching of research report content. This occurs when the
maxContextparameter is set to fewer than 6 turns, so key parameters such as category and time range from prior follow-up questions are not retained. - No recognizable text content is generated after voice input, and no voice-to-text result appears in the conversation interface. This occurs when the
VOICE_RECOGNITION_MODELparameter is not configured to a Chinese-specific model, and the automatic transcription switch for voice input is not enabled.
How to Verify Proper Configuration
- Initiate a multi-turn dialogue that includes segmented categories and time ranges, and verify that the conversation context retains key parameters from prior questions.
- Upload a single home goods research report, and confirm that no timeout error is triggered during the parsing process.
- Test the voice input function, and confirm that corresponding text transcription content appears in the conversation interface.
- Call the HTTP node that returns BLOB data, and confirm that a clickable download entry appears in the conversation interface.
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.