What the data for this category looks like
Data for this category comes primarily from public industry consulting reports, official brand-disclosed operating data, and sales monitoring data from e-commerce and offline supermarkets. Update frequency aligns with new product launches, quarterly operating milestones, and industry policy adjustments, with targeted updates at core nodes. Documents typically include industry overview, segmented category analysis (covering personal care subcategories such as facial cleansers, hair care, oral care), user behavior data, competitor developments, channel layout, and other modules. Fields include SKU code, product specification, terminal selling price, monthly shipment volume, user age range, channel type, store coverage rate, and more. Units include yuan, pieces, boxes, person-times, and others.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Industry data sources are scattered. Multi-turn dialogue must clearly define the scope of retrieval data sources to avoid mixing in research reports from unrelated categories. Update cycles are not fixed. Multi-turn dialogue must include a data timeliness confirmation step, prompting users to confirm the release date of the currently retrieved data. Documents include multiple segmented category modules (facial cleansers, hair care, etc.). Prompts must clearly specify the target segmented category to prevent cross-category retrieval confusion. Fields and units are diverse. Multi-turn dialogue must guide users to explicitly mention the unit corresponding to the field to avoid calculation errors from mismatched units. Additionally, users often follow up with requests for detailed SKU or regional segmented data. Contextual association must be retained to ensure subsequent follow-up questions can build on earlier retrieval results.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Personal care research report documents have relatively long length. Multi-turn dialogue needs to retain sufficient context to avoid losing earlier retrieved segmented category and user requirement information |
RECALL_TOP_N | Top 8–12 entries | There are many segmented categories in personal care research reports. Sufficient candidate documents must be retrieved to cover different modules and avoid missing key data |
SIMILARITY_THRESHOLD | 0.72–0.78 | Keyword similarity differentiation in personal care research reports is relatively high. Too low a value will mix in irrelevant documents, while too high a value will miss relevant segmented category content |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Single personal care research report documents typically have large length. Parsing and vector generation require sufficient time to complete, avoiding mid-run timeout interruptions |
UPLOAD_FILE_MAX_SIZE | 500 MB | Complete single personal care research report documents have large file size. The upload limit must be expanded to support importing full documents into dialogue scenarios |
RERANK_TOP_N | Top 3–5 entries | Relevant research report fragments must be quickly filtered during multi-turn dialogue to avoid redundant content interfering with contextual coherence |
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 specific analysis. It is recommended to test on your own samples before finalizing.
Three common misconfigurations
- Phenomenon: Vector index construction stalls with no clear progress feedback. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted for long personal care research report documents. Parsing timeout causes index interruption. - Phenomenon: Research report files cannot be read during dialogue, but can be recognized normally when uploaded to the knowledge base. Cause: The
UPLOAD_FILE_MAX_SIZEparameter was not adjusted. The file uploaded to the dialogue exceeds the current limit. - Phenomenon: Retrieval results are empty during multi-turn dialogue, but target content can be retrieved in separate knowledge base tests. Cause: The reasonable value range of
RECALL_TOP_NorSIMILARITY_THRESHOLDwas not limited. Multi-turn context interferes with the weight calculation of retrieval keywords.
How to confirm correct configuration
- Initiate vector index construction for a single personal care research report, check the time consumption in the parsing log, and confirm that the value of
PARSE_FILE_TIMEOUT_SECONDSis greater than the actual parsing time. - Upload a complete single personal care research report in the dialogue, confirm that the file can be loaded and parsed normally, and verify that the value of
UPLOAD_FILE_MAX_SIZEcovers the target document size. - Launch two consecutive research report retrieval dialogues. Specify the segmented category in the first round, and follow up with a request for specific field data under that category in the second round. Confirm that the context is correctly retained.
- Compare retrieval results from separate knowledge base tests and dialogue scenarios, confirm that the number of retrieved entries and relevance are consistent between the two.
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.