Reference only; verify against the target FastGPT release and deployment documentation.
What data in this sector looks like
Business data for the pharmaceutical circulation sector comes primarily from enterprise operating ledgers, drug regulatory filing systems, and scanned copies of drug packaging and qualifications. Operating ledgers include fields such as drug circulation flow, batch number, and quantity, with units including box, case, and batch number. Regulatory filing data includes structured fields such as unified social credit code, drug approval number, and business license number. Qualification data updates in real time alongside enterprise changes. Inventory and sales data updates daily. Drug catalog data updates quarterly, with a large volume of attached image materials.
What constraints these characteristics impose on model access and configuration
Pharmaceutical circulation data contains specialized pharmaceutical terminology, structured qualification and transaction fields, and a large amount of attached image materials. Data update rhythms also vary. Specialized terminology and long-text transaction records require models to have sufficient context carrying capacity to avoid information truncation. Image materials require models to support visual recognition functions. Without this support, qualification and packaging information cannot be parsed. Data with different update frequencies must match corresponding call frequencies to avoid service pressure from high-frequency calls. Structured field precision query requirements must also be adapted to.
How to set configurations
| Config Item | Recommended Value | Rationale |
|---|---|---|
vision | Enable | Pharmaceutical circulation data includes image materials such as drug packaging and business licenses. Image recognition support is required to parse relevant qualification and packaging information |
maxContext | 8000–12000 token | Pharmaceutical circulation data includes specialized terminology and long-text transaction records. Sufficient context is needed to carry complete business information |
maxTokens | 2000 token | Limit the length of a single reply to adapt to the conventional reply scope of service consultations, and avoid redundant output content |
temperature | 0.2–0.4 | Service consultations require accurate replies to policies and business processes. Excessive randomness must be avoided to prevent errors in specialized terminology |
toolChoice | auto | Automatically call tools to query drug circulation information or qualification data based on user consultations, to improve reply accuracy |
AIPROXY_API_ENDPOINT | Fill in the public network interface address of the deployment node | Adapt to locally or privately deployed model services, and ensure that data calls comply with compliance requirements |
Three common mistakes
- Phenomenon: After enabling the
visionconfiguration, no recognition result is returned when uploading drug packaging images. The interface prompts that image content cannot be provided. Reason: The image recognition switch is not enabled separately in the conversation node, or the accessed model does not support visual capabilities. - Phenomenon: A connection error occurs when connecting to the Qwen3-Embedding-8B model deployed via VLLM. Reason: The address configured in
AIPROXY_API_ENDPOINTdoes not include the correct embedding model interface path, orAIPROXY_API_TOKENpermissions are insufficient. - Phenomenon: The model occasionally interrupts output when calling tools. Reason: The
maxContextconfiguration value is too small, causing context overflow and triggering an interrupt, or themaxTokenssetting exceeds the upper limit supported by the model.
How to confirm the configuration is complete
- Upload scanned copies of drug packaging or business licenses to the conversation node, and check whether corresponding recognition results can be returned.
- Call the configured model interface, verify the connectivity of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, and confirm no error returns. - Initiate consultations involving specialized pharmaceutical terminology or circulation processes, and check whether the reply conforms to business logic and has no random errors.
- Test the tool call process, and confirm that the model can automatically trigger tools to query relevant pharmaceutical circulation data or qualification information.
The values listed here are common starting points, and should be evaluated against the samples relevant to the specific use case.
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.