What the data for this category looks like
Medical device yield rate and market trend data is primarily sourced from public listings of medical consumable reference prices released by local medical insurance bureaus, winning bid announcements from provincial government procurement bidding processes, and annual procurement ledgers of public medical institutions. Data update schedules follow local policy adjustments; some regions update reference prices monthly, while winning bid prices update with quarterly bidding cycles. Each data entry includes fields such as the general name of the medical device, registration certificate number, winning bid region, winning bid unit price, effective date of the listed price, medical insurance reimbursement category, pricing unit (e.g., yuan per unit, yuan per set), and some entries include winning batch descriptions and procurement limits.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The multi-dimensional fields of medical device data require multi-turn dialogue to first clarify the user’s query dimensions, such as procurement region, product category, and pricing unit. Otherwise, retrieved results may easily include mismatched cross-category or cross-region data. Disparate data sources and non-fixed update rhythms require the dialogue system to confirm the valid time range of data before each query, to avoid returning expired winning bid or listed prices. Diverse pricing units also require the prompt to include unit validation rules, to prevent misunderstandings regarding unit logic, such as confusing the pricing logic of yuan per unit versus yuan per set.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 2000–3000 characters | Medical device data contains multi-dimensional fields, so sufficient historical dialogue context must be retained to clarify the user’s specific query dimensions |
Number of retrieved entries | Top 8–12 entries | Medical device product models are diverse; too many retrieved entries will interfere with result presentation, while too few may miss matching precise data |
Similarity threshold | 0.75–0.85 | Medical device names have high similarity; a threshold that is too low will introduce irrelevant data, while a threshold that is too high will miss some valid approximately matched results |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Batch medical device data files usually contain large numbers of registration certificates and winning bid information, resulting in long parsing times |
Chunk length | 800–1200 characters | Medical device data includes long-text winning bid descriptions and procurement rules; chunk length adapts to the context association requirements of this type of text |
The parameter values provided on this page are conventional recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: After switching query dimensions during multi-turn dialogue, retrieved medical device data shows cross-region or cross-category mismatches. Cause: No dimension validation rule was added to the prompt before each dialogue, leading to deviations in context association.
- Symptom: In FastGPT version 4.6.9, the return output of the HTTP orchestration node is not displayed in the dialogue results. Cause: The "Output to dialogue context" configuration item of the orchestration node was not enabled; by default, results are only used for internal workflow transfer.
- Symptom: After configuring the problem classification workflow, the AI dialogue response automatically enters the problem classification node to form a loop. Cause: No branch termination configuration was added after the AI dialogue node; the workflow defaults to returning to the initial problem classification node.
How to confirm correct configuration
- Run consecutive multi-turn queries including region, product name, and pricing unit, and verify each retrieved result matches the context dimension of the current dialogue.
- Trigger the HTTP orchestration node, check if the node’s return content is displayed on the dialogue interface, and confirm the "Output to dialogue context" switch is correctly enabled.
- Test the problem classification workflow, run an AI dialogue, check the workflow path, and confirm it does not automatically return to the problem classification node.
- Upload a batch of medical device data files, wait for parsing to complete, and confirm the synchronized fields in the knowledge base are complete.
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.