What the data for this category looks like
Business data for pharmaceutical distribution primarily comes from the drug circulation registration system, enterprise inventory and supply chain management system, cold chain transportation monitoring platform, and national drug approval number database. Data is divided into two categories: static and dynamic.
Static data includes generic drug names, brand names, approval numbers, manufacturing enterprises, circulation license numbers, and similar information, with an update frequency of once per month. Dynamic data includes real-time inventory quantities, transportation temperature, batch expiration dates, outbound records, and similar information, with an update frequency of every 5 minutes.
The structure of individual data documents is fixed. Fields include Chinese names, numeric units (such as pieces, boxes, degrees Celsius), and date fields, with no nested complex structures.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering link
The difference in update rhythms between static and dynamic data requires distinguishing data types during multi-turn dialogue and triggering corresponding retrieval timing to avoid using expired inventory information.
Fields contain professional medical terms and standardized units. Prompts need to pre-map proper nouns to ensure accurate matching of field names during retrieval.
Individual data has many fields. Multi-turn dialogue must retain contextually associated drug IDs and batch numbers to avoid cross-session confusion of retrieval results.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Question Optimization | Enabled, with standardized mapping for medical professional terms | Pharmaceutical distribution data contains a large number of proper nouns, and unifying terminology formats is required to improve retrieval accuracy |
| Maximum Context Length | 8000–12000 token | Pharmaceutical data has many fields, and key information such as drug IDs and batch numbers in multi-turn dialogue must be retained |
variables Variable Binding | Bind the current session's drug ID and circulation batch number | Accurately match dynamic data such as inventory and transportation for the corresponding batch |
chatId Session Association | Persistently save session context | Ensure that context information is not lost during multi-turn dialogue and avoid cross-session retrieval confusion |
| Single Retrieval Recall Count | Top 6 entries | Balance retrieval efficiency and information integrity, avoid redundant data interfering with large model output |
| Context Refresh Interval | 300 seconds | Match the 5-minute update frequency of dynamic data to ensure that the latest inventory information is used for retrieval |
Three Common Misconfigurations
- Knowledge base retrieval returns no results during dialogue, but the knowledge base test can recall the content. The symptom is that the frontend displays no matching content, and logs show that the retrieval parameters do not carry the current session's
variablesdrug ID. The cause is that session variables are not bound, leading the retrieval scope to cover all data and fail to accurately match the specific batch drug requested by the user. - Dialogue returns 404 Invalid URL (POST /api/chat/completions). The symptom is that a 404 status code is returned when initiating a session request, and logs show that the interface path does not match the deployment configuration. The cause is that FastGPT's reverse proxy rules are not correctly configured, causing the request to not point to the correct service address.
- Workflow execution terminates halfway. The symptom is that after the large model completes its response, the subsequent result push process does not respond, and the dialogue ends directly. The cause is that
messagescontext persistence is not enabled, causing the workflow to fail to read the previous step's retrieval results and triggering process interruption.
How to Confirm Proper Configuration
- Initiate a multi-turn dialogue including drug name and circulation batch number, check the context records to confirm that the
chatIdandvariablesparameters are correctly passed. - Manually modify a piece of pharmaceutical distribution dynamic data in the knowledge base, initiate a corresponding retrieval request to confirm that the retrieval results are updated in real time.
- Test a question containing medical professional terms to confirm that the question optimization function correctly completes term mapping and the retrieval results match the corresponding fields.
- Simulate the workflow execution process to confirm that after the large model responds, the subsequent process can normally read context information and complete execution.
The values provided here are common starting points and should be evaluated against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.