What the data for this category looks like
Business data for this category comes from the formula and process library, raw material batch ledger, reactor DCS system, weighing record system, LIMS laboratory management system, and warehouse system. Data is updated per production batch and each key process step, covering documents such as batch production instructions, feeding review sheets, reaction process records, cleaning records, inspection reports, deviation and change orders, and more. A single document includes fields like feeding quantity, reaction temperature, duration, cleaning inspection items, deviation description, and other relevant details. Most fields are associated with specific batch and equipment numbers, with units including kilograms, liters, degrees Celsius, hours, and others.
What constraints these characteristics impose on the "model access and configuration" workflow
The multi-batch, multi-step document structure of this category requires model access to adapt to a long context length, to avoid truncating key process and inspection data. Multi-source heterogeneous data sources require the configuration phase to support document formats exported by different systems, and unify field mapping rules. The high update frequency requires the configured model service to have stable response speed, to avoid audit delays caused by data synchronization lag. The presence of professional fields and specific units requires selecting a model adapted to fine chemical professional terminology during configuration, to reduce field recognition errors.
How to set the configuration
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapt to the total length of single-batch multi-step documents to avoid truncating key process and inspection data |
maxTokens | 2000–3000 token | Control the output length of a single audit round to ensure complete deviation judgment and compliance prompts |
quoteMaxToken | 1500 token | Limit the maximum length of source document citations to ensure audit basis is clear and not redundant |
temperature | 0.1–0.3 | Reduce output randomness to ensure consistency of professional judgments, meeting the rigorous requirements of industrial audits |
AIPROXY_API_ENDPOINT | Fill in the interface address of the locally deployed Xinference reranker model | Adapt to locally deployed model services to meet data privacy and response speed requirements |
AIPROXY_API_TOKEN | Fill in the security credential for the locally deployed model | Complete identity verification for the model service to avoid unauthorized access |
This configuration guide is written based on FastGPT v4.17.0.
Three common configuration mistakes
- Phenomenon: A reranker model deployed locally via docker-compose does not appear in the available model list on the FastGPT platform. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, or the interface address does not have cross-domain access permissions enabled. - Phenomenon: A v2 version model is configured and its corresponding name is selected in FastGPT, but the actual call still uses the v1 version model. Cause: The model interface address points to an older version service, or the model version bound to the credential has not been updated.
- Phenomenon: A 401 unauthorized error is returned when calling the reranker model. Cause: The security credential with the Bearer prefix is not fully completed, or the credential has expired and become invalid.
How to confirm the configuration is complete
- Test access to the locally deployed model service, check if the target reranker model is included in the model list returned by the platform, and verify that the interface address and credential match the configuration items.
- Upload a single typical feeding review sheet and reaction process record to trigger the audit process, and check if the output result references the correct source document fragments.
- Adjust the
temperatureparameter value, observe whether the deviation judgment results of the two audit outputs remain consistent, and verify that the parameter configuration takes effect. - Simulate high-frequency submission of multi-batch documents, check whether the response time of the model service meets expectations, and confirm that there are no timeout errors.
The values provided are common starting points and should be measured against the reader's own samples.