What this category of data looks like
This category of data is sourced from formula and process libraries, raw material batch ledgers, reactor DCS systems, weighing record systems, LIMS laboratory management systems, and warehouse systems. Documents include batch production instructions, feeding review slips, reaction process records, cleaning records, inspection reports, deviation and change orders. Data is updated per production batch or each key process step. Individual documents contain fields with clear units such as feeding amount, reaction temperature, pressure, batch number, cleaning verification number, and involve confidential formula content. The data structure is adjusted according to product category.
Constraints on Model Connection and Configuration
This category of data is updated per batch or key process step, and its structure is adjusted according to product category. It also includes process parameters with clear units and sensitive formula content. This requires model connection configuration to adapt to dynamically changing document fields, accurately identify process parameters with units, and avoid parameter matching errors. The high-frequency, short-cycle update rhythm requires configuration to adapt to short-interval index update logic, avoiding index delays that affect Q&A timeliness. Sensitive formula content requires strictly limiting the model’s external data call scope during configuration to prevent information leakage.
Configuration Settings
This configuration is developed based on FastGPT v4.17.0. Specific parameters and recommendations are as follows:
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length of single-batch process documents, avoiding context overflow that interrupts Q&A |
maxTokens | 2000–3000 token | Meets the detailed response requirements for process Q&A, covering information output for multi-step reviews |
quoteMaxToken | 1500–2000 token | Matches the core fragment length of a single process document, ensuring quoted content is complete and complies with document specifications |
temperature | 0.1–0.3 | Reduces random output, ensures consistency of process parameters and standards, and avoids deviations in process step descriptions |
AIPROXY_API_ENDPOINT | Fill in the intranet or mapped access address according to the deployment environment | Adapts to intranet deployment scenarios, ensuring stable model call links and complying with the network security requirements of fine chemical enterprises |
AIPROXY_API_TOKEN | Valid token provided by the model service provider | Completes identity verification for model access, ensures legitimate call permissions, and prevents unauthorized access |
Three Common Configuration Errors
- Uploading a 3MB process document PDF triggers an error, with the interface displaying model call failure. The cause is that
AIPROXY_API_TOKENis not configured correctly; an invalid token prevents the model service from completing request verification. - The knowledge base index progress remains stuck, which is particularly noticeable when accessing general large models in a Docker deployment environment. The cause is that the batch-by-batch update data rhythm is not adapted, and the index trigger logic does not match the short-cycle update requirements, leading to repeated scanning of old data.
- An access point error is prompted when configuring the model. The cause is that the address filled in for
AIPROXY_API_ENDPOINTdoes not include the correct API path suffix, which does not match the interface specifications of the model service provider.
How to Verify Successful Configuration
- Upload a single typical process document, trigger a model Q&A request, and check whether the returned results include the unit-bearing process parameters in the document.
- View the knowledge base index logs, and confirm that after data updates, index tasks can complete construction within the preset cycle.
- Test the model access link after intranet mapping, and confirm that FastGPT can normally call the model interface and return valid responses.
- Test Q&A requests related to sensitive formulas, and confirm that the model does not return unauthorized formula details.
The values provided are common starting points and should be measured against the reader's own samples.