What the Data for This Category Looks Like
Chemical pharmaceutical research report data primarily comes from public clinical trial registration platforms, securities firm research report databases, official disclosure documents from pharmaceutical companies, and professional medical databases. Update frequency varies significantly by content type: newly published securities research reports are updated in real time upon launch, pharmaceutical clinical progress data is updated dynamically based on recruitment and review milestones, and annual financial report data is updated on a quarterly or annual cycle.
Document structures include core compound analysis, activity parameter, clinical stage information, and commercialization analysis modules. Fields include generic compound name, half-maximal inhibitory concentration (unit: nanomoles per liter), clinical trial phase number, number of enrolled subjects, approval document number, publishing institution, and publication date.
Constraints on Multi-turn Conversation and Prompt Engineering
The professional fields and dynamic update characteristics of chemical pharmaceutical research reports impose three core constraints on multi-turn conversation and prompt configuration, plus an additional consideration:
- Research reports include activity parameters with exclusive units. Multi-turn conversations must retain contextual unit association information to prevent the model from confusing numerical values and their corresponding units.
- Clinical progress data is updated dynamically based on milestones. Multi-turn conversation workflows must call the latest data source each time, and cannot rely on single cached results.
- Field formats vary across research reports from different sources. Prompt engineering must specify unified field extraction and alignment rules in advance to ensure consistency across data from different sources.
- Long document fragments account for a large proportion of content. The context recall threshold for multi-turn conversations must be adapted to long text lengths to avoid truncating key analysis content.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Single chemical pharmaceutical research reports often contain thousands of characters of activity and clinical data. This range covers the context retention needs of multi-turn conversations and avoids truncating key parameters |
recallTopK | Top 6–8 results | Core information of chemical pharmaceutical research reports is scattered across different documents. This quantity covers the data source requirements for multiple follow-up questions in multi-turn conversations, while avoiding redundant information interfering with model judgment |
temperature | 0.1–0.3 | Retrieval of chemical pharmaceutical research reports requires precise matching of professional parameters and facts. A lower temperature improves the consistency and accuracy of responses |
globalVarSyncMode | Session-level real-time sync | Dynamically updated data from chemical pharmaceutical research reports must remain up-to-date across multi-turn conversations in the same session to avoid expired variable values |
parseChunkSize | 1500–2000 characters | Activity parameter paragraphs in chemical pharmaceutical research reports are often lengthy. This chunking length preserves complete parameter context and avoids losing associated information after splitting |
promptTemplate | Template that consistently includes unit verification and context alignment rules | Chemical pharmaceutical research reports include parameters with exclusive units. The template must explicitly require the model to retain unit information from the previous conversation to ensure consistency across follow-up questions |
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 targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: When calling the
getConversationListinterface to retrieve conversation records, theuserQueryandaiReplyfields are not bound by conversation turn, making it impossible to quickly match corresponding questions and replies. Cause: The interface request does not include the unique identifier parameter of the current session, resulting in returned records not being grouped by session context, only returning full unsorted conversation data. - Symptom: After modifying a global variable in the same session, subsequent conversation turns cannot correctly use the updated variable value. Additionally, only the current turn's question is retrieved for three consecutive prompts, with no association to historical question content. Cause: The global variable sync mode is not set to session-level real-time sync, and the context retention parameter is not enabled, causing each conversation turn to be isolated separately in terms of context and variable values.
- Symptom: When calling a large language model using variable reference methods in the AI conversation component, the temperature setting button is not displayed in the interface, making it impossible to adjust the
temperatureparameter. Cause: The advanced parameter area is hidden by default in the component configuration panel. Temperature-related parameters can only be configured after manually expanding the advanced settings menu.
How to Confirm Proper Configuration
- Initiate a conversation that includes a question about professional parameters, check if the model output retains the unit information from the previous conversation, and confirm that the context retention configuration meets the scenario requirements.
- Call the conversation record interface with the unique identifier of the current session, check if the returned records bind user questions and AI replies by conversation turn, and confirm that the session grouping configuration is correct.
- Modify the global variable and initiate a second conversation, check if the updated variable value is included in the model input, and confirm that the global variable sync mode configuration meets the scenario requirements.
- View the advanced parameter area of the AI conversation component, confirm that the temperature setting option is displayed, adjust the parameter value to verify changes in response consistency, and confirm that the parameter configuration takes effect.
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.