What the data for this category looks like
The data for this category comes from the transaction systems of e-commerce service provider backends and open merchant operation interfaces of e-commerce platforms. It updates at fixed daily times to include complete data from the previous calendar day. Each data document is presented as a structured table, containing fields such as service order volume, customer unit price, gross profit margin-related values, and customer lifetime profitability values. The units of the fields follow the standard measurement standards for e-commerce service businesses, with no additional custom measurement rules set. The total volume of data changes dynamically based on the number of merchants partnered with the service provider. The character length of a single daily report document typically ranges from several thousand to tens of thousands.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The daily fixed update requirement means that the dialogue flow must be configured with real-time data interface calls, to avoid using expired cached data. The large range of single daily report document lengths means that in some scenarios, the document will exceed the context window supported by the model. Therefore, automatic slicing and segmented recall rules need to be set for long documents. Users often ask follow-up questions about profitability data for different SKUs or merchants in multi-turn dialogues. The context association logic must be retained to ensure that subsequent questions match the previously mentioned business scope. There are diverse field types linked to business measurement rules, so the prompt must clearly define the retrieval scope and field call logic to avoid returning irrelevant data.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | Adapts to the typical length of e-commerce daily report documents, and reserves context storage space for multi-turn dialogue |
UPLOAD_FILE_CHUNK_SIZE | 1000–2000 characters | Splits long documents into processable segments, adapting to the sub-window limits of a single model input |
recall_top_k | 3–5 entries | Accurately recalls e-commerce data paragraphs related to the current question, avoiding interference from redundant information |
context_history_max_length | 10–15 dialogue turns | Matches the typical scenario of multi-turn follow-up questions from e-commerce service users, avoiding context overload |
file_parse_chunk_overlap | 200 characters | Retains overlapping content between segments, preventing business logic breaks caused by slicing |
auto_clean_history | Enabled | Regularly cleans expired dialogue history to free up context window resources |
The parameter values provided on this page are all common recommendations used as starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require case-by-case analysis, and it is recommended to test using relevant sample datasets before finalizing settings.
Three common configuration mistakes
- Phenomenon: Starting from the second question in a multi-turn dialogue, the system returns irrelevant answers that fail to associate with previously mentioned e-commerce SKUs or merchants. Cause: The
context_history_max_lengthparameter is not configured, or its value is set too low, causing the context to be truncated and unable to retain prior business context. - Phenomenon: After uploading an e-commerce daily report document, the system returns an error that the context exceeds limits, or the recalled content has missing segments. Cause: The
UPLOAD_FILE_CHUNK_SIZEparameter is not set, or the segment length is set too large, with no effective slicing processing for long documents. - Phenomenon: After multiple e-commerce daily report documents are uploaded to the knowledge base, it is impossible to specify and retrieve the content of a specific document through questioning. Cause: The retrieval scope of documents is not clearly defined in the system prompt, causing the recall logic to cover all uploaded documents.
How to confirm configurations are properly applied
- Initiate a single-turn question, enter a core content fragment of an e-commerce daily report document, and verify that the system can accurately return the corresponding business data, confirming that the document slicing and recall configurations are effective.
- Initiate consecutive multi-turn questions, asking about profitability information from different dimensions in sequence, and verify that the system can associate content from prior questions, confirming that the context retention configuration is effective.
- Upload multiple e-commerce daily report documents, explicitly specify the retrieval scope of a specific document in the question, and verify that the system only returns content from the corresponding document, confirming that the document restriction configuration is effective.
- Initiate consecutive dialogue that exceeds the preset number of turns, and verify that the system will automatically clean expired dialogue history, confirming that the automatic cleaning configuration is effective.
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.