What data for this category looks like
Biologics investment research data mainly comes from public clinical trial databases, drug regulatory agency approval documents, corporate annual reports, patent databases, and industry research reports. Update frequencies vary significantly by data type: clinical trial data updates monthly, regulatory approval documents update in real time alongside approval progress, patent data syncs weekly, and industry research reports are released quarterly. Documents include structured fields and unstructured text. Structured fields include generic name, brand name, indications, clinical trial phase, and adverse event incidence rate. Common units are mg/dose, sample size (cases), and similar units. Unstructured text includes clinical trial protocols, full regulatory approval documents, and similar content.
Constraints on multi-turn conversation and prompt engineering workflows
The multi-source dispersion of biologics investment research data, specialized structured fields and units, and lengthy unstructured text impose multiple constraints on multi-turn conversation and prompt engineering workflows. Multi-turn conversations must track biologic names and dose units from historical queries to avoid context confusion. Lengthy unstructured documents require limiting the length of context recall to prevent exceeding model token limits. Multi-source data recall must match field rules across different data sources; for example, extract approval information from regulatory files by matching the correct approval document number field. In investment research scenarios, users often initiate cross-category comparison queries. Historical session entity information must be retained to support coherent multi-turn interactions.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Biologics unstructured documents (such as clinical trial protocols) are lengthy. Excessively long segments lead to redundant context, while excessively short segments damage the integrity of technical terms. This range balances recall accuracy and context length. |
similarityThreshold | 0.75–0.85 | Biologics investment research data is highly professional. This threshold filters out low-correlation recall results and selects document fragments with high semantic matching to the query. |
recallTopK | Top 6–8 results | Investment research scenarios require a balance between comprehensiveness and context length. Too many recall results exceed model token limits, while too few miss key information. |
latexRenderEnable | Enabled | Biologics structural formulas and dose calculation formulas often use LaTeX format. Enabling this allows professional symbols to render correctly in the conversation interface, avoiding display of raw code. |
chatHistoryMaxCount | Top 10–15 sessions | Multi-turn investment research conversations often involve multiple biologic entities. Retaining an appropriate number of historical sessions maintains context coherence and prevents entity loss. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Biologics documents (such as full clinical trial reports) have large file sizes and take longer to parse. This duration covers the parsing process for most large documents. |
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 specific analysis, and it is recommended to test against one’s own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: LaTeX-formatted biologics structural formulas and dose formulas render correctly in debug preview, but only raw LaTeX code is displayed after application release. Cause: The
latexRenderEnableswitch in the knowledge base configuration is not enabled, or the configuration item was not updated during the release process. - Phenomenon: Custom biologics investment research-specific prompt engineering does not take effect, and conversation replies do not follow preset professional response specifications. Cause: The prompt engineering is not mounted to the corresponding investment research knowledge base, or the prompt engineering does not clearly define usage rules for biologics-related terms.
- Phenomenon: The front-end conversation page has multiple independent output modules, and user input and AI replies cannot be bound to a single unified module. Cause: The binding container for conversation content is not specified in the front-end configuration, or the context of multi-turn sessions is not correctly associated with a single output module.
How to Confirm Proper Configuration
- Initiate a query containing biologics dose formulas or structural formulas, and verify that professional symbols render correctly on the released conversation interface.
- Initiate a multi-turn cross-biologics comparison query, and verify that the conversation history retains all entity information and that replies are coherent without context loss.
- Upload a complete clinical trial report document, and verify that no timeout error occurs during the parsing process.
- Submit a custom investment research prompt engineering, and verify that conversation replies follow the professional response specifications set in the prompt engineering.
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.