This document is written based on the functional logic of FastGPT v4.17.0.
What this category's data looks like
Data for this category comes from water source and raw material testing, formula systems, online sugar and acidity monitoring, filling equipment, visual inspection systems, and LIMS systems. Daily documents include formula sheets, water treatment records, blending records, sterilization or aseptic filling records, packaging material inspection reports, and finished product inspection reports. Data updates occur per production batch, during shift-based inspections, and via continuous online collection. Fields include feeding amount, turbidity, filling liquid level, sealing pressure, batch number, and shelf life. Corresponding units include kilograms, NTU, millimeters, kilopascals, days, and others.
What constraints these characteristics impose on model access and configuration
Multi-source data has significantly different update cycles. High-frequency, continuously collected online data requires adaptation to high-frequency interface calls. Batch data requires associated indexing based on production batches. Model access must support mapping rules for multi-source structured data. There are many types of daily documents. Fields and units vary across different documents. Clarify the extraction logic for each document during configuration to avoid unit conversion errors. Frequent product switching leads to wide data batch spans. Model context must support associated queries across multiple batches of data. Limit the retrieval scope to datasets corresponding to the production link to ensure the accuracy of defect analysis. Key data from aseptic filling and water treatment requires high-confidence association. Avoid interference from irrelevant data during configuration.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
AIPROXY_API_ENDPOINT | http://localhost:11434/v1 | Adapts to the standard interface path for locally deployed Ollama large models, conforms to the general format for local model calls |
AIPROXY_API_TOKEN | Empty string | Most locally deployed Ollama models do not require authentication, leaving the field blank enables basic calls |
maxContext | 8000–12000 token | Adapts to the context length required for multi-batch associated data in beverage production, covers core record data from a single shift |
temperature | 0.1–0.3 | Ensures consistency in defect analysis and traceability results, avoids analysis bias caused by randomness |
quoteMaxToken | 1500–2000 token | Limits the length of retrieved document fragments, matches the character count of single documents such as formulas and inspection records |
| Workflow/API tool selection | auto | Allows the model to independently choose whether to call retrieval tools, adapts to the associated analysis requirements of multi-source data |
Three common configuration mistakes
- Calls to locally deployed Ollama models return connection failure errors, with the interface prompting that the address cannot be resolved. The cause is incorrect configuration of the
AIPROXY_API_ENDPOINTparameter, with an incorrect local address or missing port number. - When an English prompt is entered and English output is requested, the model still returns Chinese results. The cause is a mismatch with the default language setting of the locally deployed model, and failure to enforce output language rules in the configuration.
- Retrieved document fragments are too short to cover complete production batch records. The cause is unreasonable setting of the
quoteMaxTokenparameter, with a value range that is too small and causes truncation of key information.
How to confirm successful configuration
- Run a test call to the local model interface. Check that the returned response status code is 200. Confirm that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations are correct. - Enter a preset English prompt. Verify that the model output language matches the requirement. Confirm that the language-related configurations are effective.
- Upload a single production batch document. Check that the retrieved fragment length meets expectations. Confirm that the
quoteMaxTokenconfiguration is reasonable. - Simulate an associated query for multi-batch data. Check that the results returned by the model cover records from the corresponding production link. Confirm that the
maxContextconfiguration adapts to the data scale.
The values provided here are common starting points, and should be evaluated against samples relevant to the specific deployment.