What Data Looks Like for This Category
Data for this category originates from formula and process libraries, raw material batches, reactor DCS systems, weighing systems, LIMS, and warehouse management systems. Daily documentation includes batch production instructions, feeding verification slips, reaction process logs, cleaning records, inspection reports, and deviation and change orders. Data is updated per batch and each critical step. Fields include batch number, raw material batch identifier, feeding quantity, reaction temperature and duration, inspection results, deviation records, and additional relevant fields. Field units follow definitions from each source system. Document structures differ slightly across batches, and confidential formula-related content is included.
Constraints on Model Access and Configuration
The multi-batch switching, heterogeneous data sources, and frequent updates associated with this category impose multiple constraints on model access and configuration. First, multiple scattered system data sources require unified access, so tool call rules adapted to multi-source heterogeneous data must be configured. Second, confidentiality requirements for each batch of data limit data outflow scope, so intranet proxy and data desensitization rules must be configured. Third, the batch-based update feature requires context isolation per batch to prevent cross-batch data mixing. Fourth, real-time data from critical steps requires adaptation to high-frequency update access frequencies, balancing response latency and data timeliness.
How to Set Configuration
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Accommodate the full context of ledger documents for a single batch, avoiding truncation of core information such as critical feeding and reaction parameters |
maxTokens | 4000–6000 token | Balance output completeness of single-batch reports and response speed, adapting to the content length of ledger summarization tasks |
temperature | 0.1–0.3 | Ensure accuracy of ledger summarization, reduce the probability of generating fabricated content, and meet the rigor requirements of production data |
| Workflow/API tool selection | auto | Automatically determine whether to trigger tools such as database queries and system calls, adapting to aggregation needs for multi-source data |
quoteMaxToken | 2000–3000 token | Retain sufficient original database text fragments to achieve reference effects similar to RAG knowledge bases |
AIPROXY_API_ENDPOINT | Enterprise internal dedicated proxy address | Adapt to intranet deployment environments, ensuring confidential content such as formulas does not flow outside the enterprise network |
AIPROXY_API_TOKEN | Exclusive access key assigned by the enterprise | Verify model call permissions and ensure access security |
Three Common Misconfigurations
- Symptom: After a Function CALL calls a database, the returned result does not include original database text fragments, only summarized content. Cause: The
quoteMaxTokenparameter is not configured, or its value is set too small, resulting in truncation of original data fragments. - Symptom: The specified new model version cannot be found in the channel configuration list, and model access cannot be completed. Cause: Custom model channels are not configured via
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, and only the built-in fixed channel list is used. - Symptom: Cross-batch ledger data mixing occurs in multi-turn conversations, with feeding and reaction parameters from different batches displayed together. Cause: Context scope is not divided by batch dimension, or the
maxContextparameter does not limit the maximum length of single-batch context.
How to Confirm Successful Configuration
- Submit a summarization request for a single batch of ledger data, check whether the returned result covers all required document fields, such as feeding quantity, reaction parameters, deviation records, and additional relevant fields.
- Call the Function CALL tool to query a specified database table, confirm that the returned result includes original database text fragments that meet reference requirements.
- Submit two consecutive cross-batch conversations, verify that the context only retains relevant data for the current batch, with no redundant information from other batches.
- View the FastGPT v4.17.0 model call logs, confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly, with no network connection errors.
The values provided are common starting points and should be evaluated against the reader's own samples.