What the data for this category looks like
Business data for refined oil sales mainly comes from fuel station inventory systems, employee attendance management platforms, fuel card transaction systems, and site inspection archives. Data update rhythms fall into two categories: transaction data is synced in real time or via daily batches, while inspection and scheduling data is updated per shift. The structure of individual business documents is fixed, including fields such as oil product type, transaction volume (unit: liters), transaction unit price (unit: yuan per liter), transaction time, site ID, employee ID. Some documents include associated IDs and status tags for rectification records.
What constraints these characteristics impose on model integration and configuration
Multi-source synchronization of business data and diverse fields require parsing logic adapted to multiple data formats during model integration. Fixed enumerated oil product type fields need preset enumerated value constraints in model configuration to avoid non-standard output. High-frequency call scenarios for real-time transaction data require adjusting model request timeout parameters to meet low-latency requirements. Multi-document associated site inspection rectification scenarios require configuring context association rules for tool calls, ensuring the model can accurately match site IDs and rectification records. Differences in data update real-time performance require distinguishing retrieval thresholds for batch and real-time data in configuration, avoiding redundant retrieval that consumes resources.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the number of fields and content length of multi-source business documents, preventing information loss caused by context truncation |
maxTokens | 2000–3000 token | Matches single-round reply requirements for refined oil sales scenarios, covering typical output lengths such as order parsing and inspection rectification suggestions |
quoteMaxToken | 1500 characters | Limits the length of retrieved and referenced document content, adapting to the average character count of single documents such as fuel station transactions and inspections |
toolChoice | auto | Allows the model to independently choose whether to call tools to process associated data, adapting to multi-document associated business scenarios |
temperature | 0.3–0.5 | Reduces generation randomness, ensuring output accuracy of enumerated fields such as oil product types and site IDs |
AIPROXY_API_ENDPOINT | Fill in according to actual deployment, such as http://localhost:8000/v1 | Adapts to the port configuration of vllm inference services, consistent with the port mapping rules of docker compose deployment |
Three common mistakes
- Incorrect port path when configuring
AIPROXY_API_ENDPOINT, resulting in503 Service Unavailableerror. The cause is failure to match the 8000 port exposed by the vllm service in docker compose, or failure to add the correct API path prefix. - Knowledge base retrieval response timeout, with single-round retrieval time exceeding the allowable range of the business. The cause is failure to adjust the
quoteMaxTokenparameter according to the number of fields in multi-source refined oil data, leading to loading excessive redundant document content during retrieval. - Failed normal call when accessing the distilled DeepSeek model, returning
invalid api formaterror. The cause is failure to adapt to the special request format of the distilled model, directly using the access configuration of general large models, and failing to adjust the parameter structure of the request body.
How to confirm the configuration is complete
- Initiate a test call, input a business question containing oil product type and site ID, check whether the model output includes preset enumerated field values.
- View retrieval logs, confirm that the number of returned reference documents matches the
quoteMaxTokenlimit in the configuration, with no abnormal content truncation. - Verify the tool call function, input a question involving multi-document association, check whether the model can correctly trigger tools and return associated results.
- Check the validity of the API key configuration item
AIPROXY_API_TOKEN, confirm that there are no authentication failure log records during calls.
The values provided are common starting points and should be measured 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.