What data for this category looks like
Communication tower and infrastructure business processing data primarily comes from operation and maintenance ticket systems, base station approval platforms, and inspection archive documents. Data is updated in real time along with business processing nodes. Ticket-related data synchronously modifies status and attachments as the process advances, while inspection records are synced in batches weekly. Each document includes fields such as base station ID, ticket number, processing link, list of application materials, current progress, and legal processing time limit. The base station ID is a pure numeric code, processing time limits are measured in calendar days, and the status field uses fixed enumerated values.
Constraints imposed by these characteristics during model access and configuration
Communication tower and infrastructure business processing data includes fixed enumerated processing statuses, processing time limits measured in calendar days, precisely matched base station IDs and ticket numbers, and is updated in real time along with business nodes. These characteristics require that model output must strictly match preset enumerated values to avoid generating non-standard business statuses. Clear time unit rules must be established to prevent confusion between calendar days and other time expressions. Precise field matching must be prioritized during the retrieval phase to ensure the accuracy of business data. Support must be provided for rapid access to real-time updated ticket data to avoid calling outdated information. Therefore, model constraints and retrieval logic must be adjusted specifically during configuration.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long-text context of complete tickets, approval materials, and inspection records during business processing, ensuring full coverage of end-to-end process information |
maxTokens | 2000–3000 token | Limits the output length of single-round process guidance to avoid information redundancy, meeting the concise prompt requirements of business processing |
quoteMaxToken | 1500–2000 token | Matches the average length of a single business document, ensuring retrieved reference materials are complete and do not exceed the context window limit |
temperature | 0.1–0.3 | Reduces generation randomness to ensure business process steps strictly match preset rules and avoid generating non-standard processing paths |
AIPROXY_API_ENDPOINT | Local Docker-deployed model address or private cloud gateway address | Adapts to the call requirements of privately deployed communication tower business systems, avoiding delays and security risks from public network access |
AIPROXY_API_TOKEN | Exclusive key for privately deployed models | Ensures the security of interface calls and prevents unauthorized access to sensitive data of communication tower business systems |
Three common configuration mistakes
- Symptom: Connection timeout occurs when calling a privately deployed large model, and the interface displays "504 Gateway Timeout". Cause:
AIPROXY_API_ENDPOINTwas not configured as the local or virtual private cloud address of the private model, causing requests to fail to reach the target model service. - Symptom: The model cannot be selected after configuring the classify module, but it runs normally after publishing. Cause: The corresponding privately deployed index model and dialogue model were not bound during the model access phase, and configuration information was only loaded when the workflow was published.
- Symptom: Calls to the locally deployed M3E index model fail, returning "invalid model path". Cause: The deployment path of the private index model was not correctly specified in FastGPT configuration, causing failure to load local embedding model files.
How to verify correct configuration
Reference only; verify against the target FastGPT release and deployment documentation.
- Initiate a simulated base station business processing query, check whether the process steps returned by the model match preset enumerated status and processing time limit rules, and verify that the model output format constraints take effect.
- View FastGPT's model call logs to confirm that interface requests to the private model can connect normally, with no connection timeout or permission errors, and verify that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations are correct. - Upload a real communication tower business ticket document to test whether the retrieval results include complete application materials and processing link information, and verify that
quoteMaxTokenandmaxContextconfigurations adapt to document length. - Adjust the
temperatureparameter and generate multiple rounds of responses, observe the consistency of generated content, and confirm that the randomness of model output meets business guidance requirements.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.