What the Data for This Category Looks Like
Pharmaceutical investment research data for financial investment scenarios mainly comes from public patent databases, CDE review public notices, clinical study reports, pharmaceutical company annual financial reports and R&D pipeline announcements. Data updates are event-triggered: patent applications are updated in real time, clinical trial data is released quarterly, and pharmaceutical company pipeline dynamics are adjusted as needed. Document structures include structured fields and unstructured text. Structured fields include trial ID, IC50 value, administration dose, trial duration, etc., with units including molar concentration, milligrams per kilogram, days, etc. Unstructured text includes trial protocols, adverse reaction descriptions, and other content.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
The multi-source heterogeneous data structure of pharmaceutical investment research requires multi-turn dialogue prompts to clearly distinguish between structured field extraction and unstructured text summarization scenarios, to avoid information confusion. Real-time updated patent and pipeline data requires dialogue context to bind the timestamp of the latest retrieval node, to prevent calling outdated information. The presence of multi-unit fields such as molar concentration and administration dose requires active verification of unit consistency in multi-turn dialogue, to avoid comparison or calculation errors. Length limitations of long documents such as full clinical reports require dialogue context truncation rules to prioritize retaining key information such as trial core endpoints and administration plans, to ensure that content required for investment research decisions is not lost.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | A single clinical report in pharmaceutical investment research often spans thousands of characters, so key information such as trial IDs and administration plans in multi-turn dialogue must be retained to avoid core content being truncated |
recall_top_k | Top 6–8 entries | Pharmaceutical investment research requires comparison of multiple sets of trial data. Too many recalled entries increase context load, while too few fail to cover key comparison dimensions |
prompt_template | Structured output according to "trial scenario + field requirements", with clear unit verification steps | Multiple unit fields exist in pharmaceutical investment research data, so the prompt must enforce unit consistency verification logic to avoid deviations in investment research conclusions |
LLM_MODEL | Professionally fine-tuned models that support long context | Pharmaceutical terminology is highly specialized, requiring models to understand professional concepts such as IC50 and clinical trial endpoints, and adapt to long document processing needs |
global_variable_scope | Only valid within the current investment research workflow | Pharmaceutical investment research workflows are often bound to specific pipelines or trial groups, and cross-workflow global variables will cause data confusion |
token_calculation_mode | Conversion based on actual input and output character count | Pharmaceutical documents are dense with terminology, so accurate token consumption statistics are required to control call costs and avoid exceeding budgets |
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 Errors
- Phenomenon: The workflow returns null values or unbound expected data when calling global variables. Cause: The scope of global variables is not limited to the current investment research workflow, leading to cross-workflow variable conflicts.
- Phenomenon: The AI dialogue node returns the
chat:LLM_model_response_emptystatus code, with no response output for multiple consecutive times. Cause: Reasonable context truncation rules for long pharmaceutical documents are not configured, causing large model context overflow and triggering response interruption. - Phenomenon: Unclosed reference mark garbled characters appear at the end of AI output text, and subsequent output automatically corrects to standard quotation marks. Cause: The prompt does not clearly specify the format specifications for structured output, and does not restrict the use scenarios of special symbols, leading to incorrect parsing of brackets or reference symbols in professional terminology.
How to Confirm Proper Configuration
- Initiate a multi-turn dialogue containing multiple sets of trial data, check whether the output content includes the previously mentioned trial IDs and administration doses, to confirm that context memory functions normally.
- View the token consumption log, check whether the token calculation rules match the configured items, to confirm that the billing logic meets expectations.
- Input test text containing multiple unit fields, check whether the output actively verifies unit consistency, to confirm that the prompt template is effective.
- Simulate three consecutive calls to the large model, check whether no response or garbled output occurs, to confirm that the exception handling 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.