What the Data for This Category Looks Like
Data related to biologics yield rates comes from three sources: National Medical Products Administration batch release public data, periodic reports of listed biologic product enterprises, and third-party industry monitoring platforms. Update schedules fall into three categories:
- Batch release data updates weekly
- Quarterly financial report data updates quarterly
- Overall industry monitoring data updates every two weeks
A single document typically includes fields such as common product name, dosage form, specification, manufacturing enterprise, total batch release volume, unit selling price, and revenue proportion. Field units include dosage units (vial/bottle), monetary units (yuan), quantity units (ten thousand units/ten thousand vials), and others. Field order and level of detail vary across different documents.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompting
Dispersed multi-source data requires associating context across dimensions such as batch release, financial reports, and third-party monitoring during dialogue. Without configured context association rules, logical confusion across time dimensions is likely to occur.
Data with different update frequencies (weekly batch release data and quarterly financial reports) requires clear time range restrictions in dialogue. Without such restrictions, short-term fluctuations and long-term trends are easily confused.
The category has high field complexity. Multi-turn questioning requires precise matching of specific fields such as product name, cost, and revenue proportion. Without preset field mapping rules, irrelevant responses are likely to occur.
Multiple documents in a single knowledge base (financial reports of different biologic product enterprises) require support for specifying retrieval targets. Without configured document filtering rules, precise recall of product data from the target enterprise is not possible.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxConversationContext | First 12 turns of dialogue context (approximately 8000–10000 tokens) | Adapts to the requirement of multi-field, multi-time dimension questioning for biologics, and avoids logical breaks caused by context overflow |
recallTopK | Top 7 recall results | Biologics documents have many fields. Too many recall results increase the large model's processing burden, while too few fail to cover critical data |
similarityThreshold | 0.70–0.75 | Balances matching accuracy and recall range, and adapts to matching requirements for fields with similar naming such as product names and specifications |
promptTemplate | Fixed template preconfigured with "clear data time range", "specify retrieval document tags", and "limit field types" | Customization is only supported in V4.9.0 and later versions. Adapts to the requirements of multi-source data and multi-document retrieval, and avoids confusion between data of different cycles and enterprises |
conversationHistoryRetentionDays | 14 days | Adapts to the update schedule of biologics batch release data (weekly) and quarterly financial reports (every two weeks), and avoids expired context interfering with current dialogue |
fileRetrievalMode | Tag-filtered retrieval | Accurately locates target biologics documents in a single knowledge base, and avoids cross-enterprise data interfering with retrieval results |
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 case-by-case analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Phenomenon: Irrelevant responses starting from the second question in multi-turn dialogue, unable to associate field information from previous conversations. Cause: The
maxConversationContextparameter is not configured, or its value is set too small, leading to insufficient context window to retain key information from prior conversations. - Phenomenon: Retrieval results include content from non-target biologic product documents, unable to precisely recall product data from the specified enterprise. Cause: The tag filtering function of
fileRetrievalModeis not enabled, or the identifier of the target document is not specified in the prompt template, leading to recall of cross-enterprise data. - Phenomenon: Excessive system resource usage from conversation history, or expired quarterly financial report data interfering with logical judgment of current dialogue. Cause: The
conversationHistoryRetentionDaysparameter is not configured, or the retention days are set too long, including expired business data.
How to Verify Proper Configuration
- Initiate multi-turn questioning that includes time ranges and specific biologic product names, check whether the large model can associate field information from previous conversations to confirm that the context configuration is effective.
- Specify the exclusive tag of a specific biologic product document in the prompt template, check whether the retrieval results only include content from that document to confirm that the document filtering and prompt template configuration are effective.
- View the conversation history management interface, confirm that records exceeding the preset retention days have been automatically cleaned up to confirm that the conversation history retention configuration is effective.
- Adjust the
similarityThresholdparameter, test the number of retrieval results under different thresholds to confirm that the configuration works properly.
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.