What the Data for This Category Looks Like
E-commerce service research report data comes primarily from e-commerce platform transaction monitoring databases, brand sales ledgers, and public documents from third-party retail research institutions. Data update frequency follows two patterns: core transaction data is synced daily; industry trend reports are updated monthly; special track reports are released on demand. Each research report document includes a title, publishing entity, release date, core category indicators, user behavior data, and supply chain reference data. Fields include monitoring cycle, category sales revenue, user visit frequency, and inventory turnover days, with corresponding units of day, yuan, visits per person, and days respectively.
Constraints for Multi-turn Dialogue and Prompt Engineering
The daily updated transaction data of e-commerce service research reports requires multi-turn dialogue to limit retrieval time ranges. Prompts must explicitly specify that only updated documents from a specified recent period are called, to prevent returning outdated data. Multi-dimensional category indicator fields require multi-turn dialogue to support layered, detailed follow-up questions. Prompts must pre-set a callable field list to avoid the model generating uncovered indicators. The document’s abstract and detailed section structure requires prompts to guide the model to first extract core conclusions, then retrieve detailed content from corresponding sections based on user follow-up questions. It is also necessary to control the context window length to avoid redundant information interfering with dialogue logic. Additionally, research report sources include third-party institutional documents. Prompts must clearly specify citation rules for data sources to ensure response credibility.
How to Configure
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Single e-commerce research report documents have relatively long average lengths. Multi-turn dialogue requires retaining sufficient historical interactions and retrieval results to avoid context truncation causing loss of critical information |
retrieve_top_k | Top 6–8 results | There are many niche tracks in e-commerce research reports. Excessive recall leads to information overload, while insufficient recall fails to cover the segmented data required by users |
context_retrieve_time_range | Last 30 days | E-commerce industry data has strong timeliness. Updated data from the last 30 days meets the real-time analysis needs of most users |
parse_chunk_size | 1500–2000 characters | Core indicator paragraphs in e-commerce research reports are relatively long. An appropriate chunking length preserves the complete logical connection of indicators, avoiding splitting that disrupts data logic |
rerank_top_n | Top 3–4 results | Reranking filters irrelevant research reports, retains the most relevant content, and avoids information confusion during multi-turn dialogue |
system_prompt_template | Pre-configured e-commerce research report retrieval template, including field list and citation rules | Must clearly limit the research report fields that the model can call to avoid generating irrelevant content, and standardize the annotation method for data sources |
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 settings.
Three Common Mistakes
- The symptom is that multi-turn dialogue returns research report data unrelated to the current query, and the number of results does not meet expectations. The cause is that the value range of the
retrieve_top_kparameter is not configured, leading to excessive recall of irrelevant research report documents. - The symptom is that the top title of the embedded mini-program dialogue page is automatically modified and cannot be restored. The cause is that the
custom_titleconfiguration item is not added to the embedded code, and the platform loads the system default title by default. - The symptom is that AI responses include research report indicators outside the preset field list. The cause is that the system prompt does not clearly limit the callable field list, leading the model to generate content not included in the knowledge base.
How to Confirm Proper Configuration
- Initiate a query with clear time requirements, and verify that the release dates of returned research reports fall within the preset time range.
- Initiate layered, detailed follow-up questions, and verify that the model only calls indicators from the preset field list and does not generate irrelevant content.
- Test the title configuration of the embedded code, and verify that the title displayed at the top of the page matches the custom configuration.
- View the dialogue context record, and verify that historical interaction content is fully retained and no redundant information appears.
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.