What the data for this category looks like
Production data sources for this category include water source and raw material testing, formula systems, online sugar and acidity monitoring, filling equipment, visual inspection systems, and LIMS systems. Daily ledger documents include formula sheets, water treatment records, blending records, sterilization or aseptic filling records, packaging material inspection sheets, and finished product inspection sheets. Fields cover sugar content values, acidity values, filling liquid level parameters, batch numbers, aseptic parameters, and more. Update frequency is per batch, per shift, or continuous collection. Ledger data volume per batch accumulates rapidly with production rhythm, and some records require synchronized data from multiple systems.
What constraints these characteristics impose on model access and configuration
Ledger data sources for this category are scattered and update quickly, requiring simultaneous processing of multiple documents from different systems. Therefore, model input context capacity must adapt to the total data volume of the full single-batch production process. Most fields are precise numerical values with units and production identifiers, and reports must strictly match field definitions, so model generation accuracy requirements are high. Continuously collected real-time data accumulates rapidly with production rhythm, so token consumption per single call must be limited to avoid exceeding the model’s context upper limit. Some records involve aseptic control and additive dosing, so parsing result accuracy must be ensured, so the model’s randomness parameter must be kept within a low range.
How to configure the settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total data volume of multiple ledgers in a single batch full process, avoiding input truncation |
maxTokens | 4000–6000 token | Matches the conventional generation length of production summary reports, avoiding forced truncation of output |
temperature | 0.1–0.3 | Reduces generation randomness, ensures the accuracy of ledger fields and values, and complies with production compliance requirements |
vision | Disabled | Most ledger documents for this category are in text format, with no embedded images or visual parsing needs, avoiding invalid parsing resource consumption |
AIPROXY_API_ENDPOINT | Fill in the API access address of the locally deployed large model | Enables access to locally deployed large models, adapting to enterprise internal deployment requirements |
AIPROXY_API_TOKEN | Fill in the access key for the local model | Verifies model access permissions, ensuring legitimate calls to locally deployed large models |
Three common configuration mistakes
- The interface displays a request failure, and the task terminates after parsing takes more than 2 minutes when a ledger is uploaded. The cause is that the
maxContextsetting does not cover the total data volume of the single batch full process ledger, preventing the model from loading complete input, or the local model’sAIPROXY_API_ENDPOINTconfiguration is incorrect, making a connection impossible. - The model call prompts that the token limit is exceeded, and report generation fails. The cause is that
maxTokensandmaxContextare not configured according to the token counting rules of the local model, causing the total actual input and output token count to exceed the model’s supported range. - After enabling
vision, uploading a document with embedded images yields no parsing results. The cause is that the access parameters of the visual parsing model are not configured, or this category of ledgers does not require image parsing functions, and enabling it will attempt to parse invalid content, causing task exceptions.
How to confirm the configuration is correct
- Upload a single standard formula sheet document, trigger the ledger summary task, and check that the task status shows completed with no error prompts.
- View the model call logs in FastGPT v4.17.0, confirm that the actual used context and token values do not exceed the configured upper limits.
- Enter a query containing additive dosing fields, confirm that the returned results include accurate field names and corresponding numerical units.
- Test the local model access, check that the connection status of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENis normal, with no permission errors.
The values provided here are common starting points and should be measured against the reader's own samples.