What this category of data looks like
It usually includes multiple types of documents such as filing materials, operation subsidy applications, service quality inspection forms, safety hazard rectification ledgers, and elderly admission records. Data is sourced from elderly care service information platforms, civil affairs statistics systems, fire inspection records, and institution operation ledgers. Data is updated on a monthly cycle. Document structures cover fields including institution basic information, service data, safety inspection items, subsidy application materials, etc. Field units are mostly institution names, number of beds, number of rectification items, application batches, etc. There is no unified fixed format template.
What constraints do these characteristics impose on the model access and configuration link
Reference only; verify against the target FastGPT release and deployment documentation.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the multi-document concatenation length for monthly summaries, covering the total character count of multiple types of materials including filings, rectification records, and subsidy applications |
maxTokens | 2000–3000 token | Controls the output length of a single supervision report, meeting the document length requirements for daily review |
quoteMaxToken | 1000–1500 token | Retains complete information of original ledger fields, ensuring core content such as bed data, rectification items, and subsidy applications is accurately referenced |
temperature | 0.1–0.3 | Reduces generation randomness, ensuring review consistency of supervision data, and avoiding incorrect amount or quantity descriptions |
AIPROXY_API_ENDPOINT | Compliant domestic node address | Adapts to compliance requirements for civil affairs supervision data transmission, avoiding compliance risks from cross-border data transmission |
AIPROXY_API_TOKEN | Exclusive key assigned by the platform | Ensures identity verification security for API calls, preventing unauthorized access to sensitive supervision data |
Three common configuration mistakes
- Phenomenon: Log errors of the type
cannot read properties of undefinedoccur when calling external models. Cause:AIPROXY_API_TOKENorAIPROXY_API_ENDPOINTis not configured correctly, resulting in missing identity verification or address parameters during API calls. - Phenomenon: Core ledger fields are missing or mismatched in the generated report. Cause: A suitable
quoteMaxTokenis not set, causing key fields of original documents to be truncated and preventing the model from accurately identifying and associating them. - Phenomenon: Vector model selection is confused, and a multimodal vector model is mistakenly selected for pure text ledger data. Cause: It is not clarified that this category of data is mainly text-based ledgers, and only a small amount of image data does not require mandatory use of a multimodal vector model.
How to confirm the configuration is complete
- Import a single elderly care institution filing material as test input, and verify that the generated summary includes core fields such as institution name and number of beds.
- View API call logs to confirm that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfiguration parameters match the values assigned by the platform. - Adjust the
temperatureparameter to around 0.2, generate the same test data multiple times, and check the consistency of the output results. - Import the monthly updated test ledger dataset, and verify that the model can correctly summarize field information from different documents and generate compliant supervision reports.
The values provided here 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.