What the data for this use case looks like
Coal transportation and sales business data primarily comes from internal enterprise transportation and sales management systems. It includes weighing slips, transportation and sales contracts, freight settlement documents, rail car scheduling records, and similar types. Data is synchronized in daily batches or after each completed transport trip. Document structures primarily use structured tables, with fixed business fields including cargo name, shipment origin mine, destination, actual shipped tonnage, settlement unit price, transport distance, settlement amount, and others. Field units follow industry standard formats such as tons, yuan per ton, kilometers, and yuan.
Constraints for Model Integration and Configuration
The structured data characteristics and update rhythm of coal transportation and sales create core constraints for model integration and configuration. Fixed business fields and units require model output to strictly match field formats. Define clear field mapping rules during integration to avoid generating content that does not comply with business specifications. A single batch of transportation and sales data includes multiple detailed field sets, resulting in a high token count for single inputs. Limit context and output length to prevent model inference resource overload. Data updates in batches, so knowledge base synchronization cycles must match business update rhythms to ensure the latest transportation and sales data is available during model calls. Some inquiries require sorting multiple batches of transportation and sales data, so additional configuration of rerank model integration parameters is needed.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Coal transportation and sales documents include multiple batches of cargo categories, tonnage, transport distance and other fields. A single batch of data has a high token count, so sufficient context must be reserved to accommodate historical documents and current requests |
maxTokens | 2000–3000 token | Service inquiries require standardized responses. Excessively long output is prone to deviating from the business scenario; limiting output length ensures responses are concise and accurate |
temperature | 0.1–0.3 | Coal transportation and sales business requires strict matching of data fields and rules. A lower temperature ensures response consistency and avoids randomly generating irrelevant content |
AIPROXY_API_ENDPOINT | Interface address of the deployed large language model and rerank model | Must match the model call requirements for coal transportation and sales scenarios; the rerank model must support the rerank interface to adapt to multi-document sorting |
AIPROXY_API_TOKEN | Exclusive interface key assigned by the platform | Ensures compliance of model call permissions and prevents unauthorized access |
quoteMaxToken | 1500 token | Transportation and sales document content must be referenced as a response basis; limiting the token count of referenced content avoids context overflow |
Three Common Misconfiguration Issues
- Symptom: The interface displays Invalid URL (POST /v1/rerank), and rerank model calls fail. Cause:
AIPROXY_API_ENDPOINTis not correctly configured to the actual deployment address of the rerank model, or the interface path does not include the /v1/rerank endpoint. - Symptom: Uploaded transportation and sales document data is not included in responses, and responses only use content from the general knowledge base. Cause: Uploaded data is not correctly associated with the input parameters of the current session, so the model cannot access the uploaded business data.
- Symptom: Responses time out and reply speed is too slow after deploying a 70B large language model. Cause: The
maxContextvalue is not limited. An excessively large context causes excessive model inference resource usage, leading to timeouts.
How to Confirm Successful Configuration
- Call the FastGPT v4.17.0 test interface, input a single batch of transportation and sales document data, and check that the response accurately references the fields and values in the document.
- Submit an inquiry that requires sorting, such as "Sort the coal shipments this week by transport distance", and check that the rerank model is correctly called and sorted results are returned.
- View model call logs to confirm there are no spelling errors or permission exceptions in the configuration of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN. - After uploading batch transportation and sales data, initiate an inquiry and check that the response includes statistical content of the uploaded data, confirming the data successfully participated in model inference.
The values provided here are common starting points and should be evaluated against the user'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.