What the data for this category looks like
Reference only; verify against the target FastGPT release and deployment documentation.
Data updates occur every 15 minutes to 1 hour. Each single ledger record includes fields such as unit number, material name, feed rate, discharge rate, operating temperature, operating pressure, and laboratory indicator values. Units include cubic meters, tons, degrees Celsius, megapascals and other professional metering standards. Each record is linked to production batch number and operating shift information. Full ledger data for a single batch can reach thousands of characters.
What constraints these characteristics impose on model access and configuration
Multi-source heterogeneous data sources require model access to be compatible with different formats of ledger export files. Clear field mapping rules must be defined to avoid recognition deviations.
The high-frequency update feature requires the context window to accommodate multiple recent consecutive ledger records. This prevents critical production data from being truncated.
The presence of professional fields and units requires prompts to clearly specify field parsing rules. This avoids the model outputting units or values that do not meet refinery and chemical industry standards.
The large data volume per single batch requires configuration parameters to balance context capacity and generation efficiency. This prevents call timeouts caused by overly long contexts.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Full ledger data for a single refinery batch has a large volume. This range accommodates multiple consecutive production records to avoid critical data truncation |
maxTokens | 4000–6000 token | Ledger summary and report generation require complete analysis conclusions and data support. This range reserves sufficient generation length |
quoteMaxToken | 2000–3000 token | Sufficient original ledger quote content must be retained to ensure traceability of data sources in reports |
temperature | 0.1–0.3 | Refinery and chemical production scenarios require output with accurate professional terms and unified formatting. This range reduces generation randomness |
AIPROXY_API_ENDPOINT | Fill in the internally deployed proxy access address, for example https://internal-api.fastgpt.com/v1 | Adapts to access restrictions of internal networks of domestic public sector and state-owned enterprises, ensuring data transmission security |
AIPROXY_API_TOKEN | Fill in the assigned dedicated service key | Completes identity verification for API calls, ensuring interface access permissions |
Three common errors
- When entering the workspace edit page, the error "No language model configured" appears. The cause is that
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINTare not filled in the system settings, or the configured key is invalid. - When calling the API with stream set to false and detail set to true, the returned result differs significantly from the online conversation. The cause is that
temperatureandmaxContextparameters are not configured uniformly. The online conversation uses session cached context by default, and the API call does not correctly pass the associated context parameters. - When using the doubao 1.6 model, no body content appears after outputting thinking content. The cause is that
maxTokensis configured too low. The thinking content occupies most of the generation length, leaving insufficient tokens to output formal report content.
How to confirm successful configuration
- Check whether
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare filled correctly on the system settings page. Send a test request to verify interface connectivity. - After configuring
maxContextandmaxTokens, import a set of real refinery and chemical production ledger data to test whether context loading is normal, with no truncation prompts. - Set
temperatureto 0.2, then initiate two identical ledger summary requests to verify the consistency of returned results. - Enter the workspace edit page to confirm that no "No language model configured" error prompt is displayed.
The values given 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.