Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Service consultation data for state-owned industrial funds mainly comes from state-owned capital operation filing documents, fund prospectuses, investment project ledgers, and policy approval letters. The update rhythm is monthly updates of investment progress ledgers, quarterly updates of supporting policy documents, and annual updates of overall fund operation reports. The length of single documents mostly ranges from 3000 to 8000 characters. Core fields include fund ID, investment entity, project amount (unit: ten thousand yuan), filing date, and approval status. Field formats must strictly follow the standardized specifications of state-owned asset data.
What constraints these characteristics impose on the "model access and configuration" link
The feature of long single documents requires model context configuration to support long text processing, to avoid truncation of core information. The requirements for structured fields and standardized units mean the model must strictly retain original field formats and units when generating responses, to prevent data distortion. The monthly and quarterly update cycles require the vector database synchronization cycle to match the data update frequency, to avoid lagging index data. The compliance requirements for state-owned asset data mean the model call link must comply with data non-outbound control rules, and unauthorized cross-border data transmission is prohibited.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Matches the average length of single documents for state-owned industrial funds, avoids truncation of core information such as filings and investments |
maxTokens | 4096 | Adapts to the output limit of mainstream large models, ensures completeness of policy interpretation and project responses |
quoteMaxToken | 1500 characters | Limits the length of referenced fund document fragments, prevents generation errors caused by context overflow |
temperature | 0.1–0.3 | Reduces generation randomness, ensures accuracy and consistency of responses for policies and fund data |
AIPROXY_API_ENDPOINT | Private proxy address deployed domestically | Complies with the data non-outbound compliance requirements of state-owned units, ensures stable call links |
AIPROXY_API_TOKEN | Exclusive access key generated by the proxy platform | Strictly controls model call permissions, complies with security specifications for state-owned asset data access |
Three common mistakes
- Phenomenon: Index construction fails, and the log shows a model ID format error. Cause: The model ID naming rules for the state-owned industrial fund scenario are not followed, and no scenario prefix is added to distinguish embedding models for different funds, causing the system to fail to recognize the target model.
- Phenomenon: Timeout or permission errors occur when calling Azure OpenAI models. Cause: A compliant proxy address for
AIPROXY_API_ENDPOINTis not configured, causing the domestic access link to overseas models to not comply with data control requirements of state-owned units, and the request is blocked. - Phenomenon: Abnormal growth of Mongo database storage usage. Cause: No reasonable upper limit for
quoteMaxTokenis set, causing a large number of redundant fund document fragments to be indexed repeatedly, occupying excessive storage resources.
How to confirm the configuration is complete
- View the FastGPT vector database management page, confirm that the number of associated state-owned industrial fund documents matches the actual data source.
- Initiate a test query, input a standardized question such as "What is the latest filing status of a certain state-owned industrial fund", check that the returned results include fields consistent with the original document format.
- Check the proxy call log, confirm that there are no 4xx or 5xx error records in the
AIPROXY_API_TOKENcall records. - View the container monitoring panel, confirm that the occupancy rates of storage such as Mongo and PG are within the reasonable range of daily operation.
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.