Internal Office and Employee Services for Coal Transportation and Sales: Model Integration and Configuration

What the data for this category looks like

Internal office data for coal transportation and sales mainly comes from the internal transportation and sales management system, truck scale weighing system, railway and road shipment ledger and settlement system. The data is divided into two categories: structured fields and unstructured attachments. Structured fields include coal type name, shipment tonnage, carrier vehicle ID, shipment arrival time, settlement unit price, etc. Units are mostly tons and yuan per ton. Daily truck scale weighing data is synchronized in real time. Daily shipment summary ledgers are updated daily. Full-cycle settlement data is updated monthly. Unstructured attachments are mostly truck scale ticket scans, shipment dispatch order PDFs, and settlement voucher scans.

What Constraints Do These Characteristics Impose on the "Model Integration and Configuration" Link

The multi-source, time-staged update data characteristics of coal transportation and sales bring multiple constraints to model integration configuration. Structured fields are numerous with unified units but a large total quantity, so the total number of fields carried in the context must be limited to avoid exceeding the model's token limit. Real-time synchronized truck scale data requires that model calls can quickly obtain the latest fields, so interface call parameters need to be properly configured to adapt to real-time data pulling. Unstructured truck scale ticket scans require enabling visual recognition capabilities to meet attachment parsing requirements. Long text content of monthly settlement ledgers requires proper configuration of context truncation rules to avoid content loss.

How to Configure Settings

Reference only; verify against the target FastGPT release and deployment documentation.

| temperature | 0.3–0.5 | Ensures accuracy of replies related to transportation and sales data, reducing random output deviation |

| AIPROXY_API_ENDPOINT | Fill in according to the proxy address deployed within the enterprise | Adapts to the enterprise's internal network environment, avoiding public network access restrictions |

| AIPROXY_API_TOKEN | Fill in according to the exclusive enterprise token assigned | Ensures identity legitimacy for interface calls |

Three Common Configuration Mistakes

• Enabling vision but still failing to parse truck scale ticket images. The symptom is an error prompt indicating that the attachment cannot be recognized or an empty result is returned. The cause is that the vision configuration item was not correctly enabled, or the truck scale ticket scan was not bound to the corresponding data source as a knowledge base attachment.
• The model only outputs the thinking process without returning the final result. The symptom is that the reply only includes thinking steps without business conclusions. The cause is that temperature is set too high, or the model's output format constraints are not configured, as some models prioritize outputting thinking process content by default.
• The iframe-embedded dialog box displays an English interface. The symptom is that control text and prompt copy are all in English. The cause is that the Chinese language parameter was not specified in the integration configuration, or the enterprise's internal language adaptation rules were not synchronized.

How to Confirm That the Configuration Is Complete

• Upload a truck scale ticket scan to the bound knowledge base, initiate a query including "identify the content of this truck scale ticket", and check whether structured fields such as coal type and tonnage can be extracted.
• Initiate a query including keywords from the monthly settlement ledger, and check whether the reply includes complete business content without content truncation.
• Call the configured interface, and check whether the returned results fall within the set temperature range, with no excessively random output content.
• View the interface call logs, confirm that the configurations of AIPROXY_API_ENDPOINT and AIPROXY_API_TOKEN have taken effect, and there are no identity verification-related errors.

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.