What the Data for This Category Looks Like
Medical device investment research data mainly comes from public registration certificate information published by the National Medical Products Administration, public product manuals from manufacturers, public clinical trial reports, and industry standard documents. The data updates dynamically alongside newly approved product launches and standard revisions. Single update cycles vary. Document structures mostly combine structured tables and long text, including fields such as registration certificate number, manufacturing enterprise, applicable departments, technical parameters, and validity period. Some documents include clear units of measurement, such as millimeters for device size, Pascals for pressure, and milligrams for dosage.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
The structured characteristics of medical device investment research data require multi-turn dialogue to distinguish between structured field queries and unstructured text interpretation, to avoid confusing technical parameters and applicable scenario descriptions. Dynamically updated data sources require prompts to explicitly call the latest public information, to avoid referencing expired registration certificate data. The high proportion of long text documents requires limiting the length of documents recalled in a single round, to prevent exceeding the model's context window. Fields with clear units of measurement require prompts to enforce unit consistency checks, to avoid deviations in investment research conclusions caused by unit conversion errors. The multi-dimensional attributes of clinical trial data require the dialogue flow to support layered follow-up questions, such as detailed information like enrollment numbers and follow-up cycles.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | Single documents related to medical devices (manuals, clinical trial reports) are mostly thousands of characters long. Retaining complete context avoids truncation of key information |
recallTopK | Top 8–12 results | Medical device investment research needs to cover multi-dimensional information including registration certificates, technical parameters, and clinical trials. Increasing the number of recalled results avoids missing key fields |
promptTemplate | Medical device investment research specific: Prioritize calling the latest public registration certificate and clinical trial data, clearly mark data sources, and verify unit consistency | Medical device data has high requirements for timeliness and accuracy. Mandatory constraints on model output rules are needed |
parseFileChunkSize | 1500–2000 characters | Medical device technical parameters are mostly continuous paragraphs. Too long segments increase recall difficulty, while too short segments destroy parameter integrity |
httpRequestTrigger | Trigger when the query contains keywords such as "latest", "approved", "updated", or "202X" year | Dynamic access to the latest medical device approval and standard update information is required to avoid using expired data |
tokenLimitPerChat | 12000 tokens | Multi-turn dialogue needs to retain context and recalled documents, to prevent exceeding the upper limit of the large model's context window |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: After configuring the HTTP request node in the workflow, the dialogue does not trigger the node and directly returns content generated by the large model. Cause: The trigger condition is not set correctly, or the trigger condition does not cover the keywords in the user's query.
- Phenomenon: A CORS-related error is returned when the frontend calls the dialogue interface, and the request cannot be initiated normally. Cause: The allowed cross-origin sources for the interface are not configured, or the interface deployment address does not match the frontend access address.
- Phenomenon: Unit conversion errors occur in multi-turn dialogue, such as converting millimeters to centimeters without marking the conversion process. Cause: The prompt template does not explicitly require unit consistency verification, or the recalled documents do not carry complete unit information.
How to Verify Proper Configuration
- Initiate a test query containing "newly approved medical devices", check whether the workflow triggers the HTTP request node, and view the request log to confirm whether the external data source is called.
- Upload a medical device registration certificate document, initiate a query containing technical parameters, and check whether the returned content includes complete fields and units of measurement.
- View the dialogue log to confirm that the token consumption data of each dialogue has been recorded, and the statistical logic covers the token count of prompts, recalled documents, and user queries.
- Adjust the trigger keywords, initiate a query that does not contain the keywords, and confirm that the HTTP request node is not triggered, and the answer is generated only using the local knowledge base content.
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.