What the Data for This Category Looks Like
The beverage and bottled water category covers carbonated drink, juice, tea, functional drink, and packaged drinking water factories. Daily business documents include formula sheets, water treatment records, blending records, sterilization or aseptic filling records, packaging material inspection records, and finished product inspection records. Data sources include water source and raw material test reports, formula systems, online sugar and acidity monitoring equipment, filling equipment, visual inspection equipment, and LIMS systems. Data updates follow per-production batch, per-shift patrol, and continuous equipment data collection schedules. Document fields include specific feeding amounts, test values, batch numbers, equipment operating parameters, and other related content.
Constraints Imposed on Model Access and Configuration
Multi-source heterogeneous data sources, batch/shift update rhythms, and fields that include feeding amounts and microbial test values create multiple constraints for model access configuration. First, multi-source data must adapt to index access for different formats. Corresponding parameters must be configured to match data structures. Second, batch-updated business data requires context to associate documents from the same batch. Context coverage must be limited to avoid cross-batch data interfering with audit results. Third, continuously collected equipment data has large volume. The length of single references must be controlled to avoid exceeding model processing limits. Additionally, rigorous content related to formulas and microbial tests requires adjustments to model call parameters to ensure consistent results.
Configuration Settings
This document applies to FastGPT v4.17.0. The following are model access configuration recommendations for this category:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | The total token count of single-batch business documents for this category usually falls within this range, which avoids truncation of core business data |
maxTokens | 4000 token | Audit tasks require detailed verification results. This value balances output length and call latency |
quoteMaxToken | 1500 token/entry | The core information length of a single business record matches this value, preventing a single reference from occupying too much context |
temperature | 0.1–0.3 | Audit tasks require rigorous results. This value range reduces result randomness to ensure consistent verification conclusions |
AIPROXY_API_ENDPOINT | Fill in the actual address of the deployed proxy service | Points to a valid model proxy interface to complete address matching for model calls |
AIPROXY_API_TOKEN | Fill in the key assigned by the proxy service | Completes interface identity verification to prevent unauthorized calls |
Three Common Configuration Errors
- Phenomenon: When the knowledge base is empty, the workflow returns "No reference data retrieved" and the model call produces no valid output. Cause: Retrieval trigger conditions are not configured, and model calls are not restricted to execute only after valid reference data is retrieved.
- Phenomenon: Model calls return 401 authentication failure or 404 interface not found errors. Cause:
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENis configured incorrectly, and the parameters do not match the deployed proxy service. - Phenomenon: Audit results omit key filling liquid level, water treatment parameters or formula feeding data. Cause:
maxContextorquoteMaxTokenvalues are too small, causing core business documents to be truncated and the model unable to obtain complete reference information.
How to Verify Correct Configuration
- Upload single-batch formula sheets and water treatment records to the knowledge base, run an audit test, and check whether the model output includes key information from the corresponding documents.
- Clear the knowledge base content, run an audit test, and confirm that the workflow does not call the model after retrieval fails, or returns a reasonable prompt.
- Adjust the
temperatureparameter to 0.5, compare two audit output results, and confirm that results in the lower value range have higher consistency. - Use an interface debugging tool to verify that the combination of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENcan normally call the model proxy service.
The values provided are common starting points and should be measured against the reader's own samples.