Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Data for policy and regulatory basis Q&A for state-owned industrial funds comes primarily from official public documents issued by the State-owned Assets Supervision and Administration Commission of the State Council, the Ministry of Finance, local state-owned assets supervision and administration commissions, and industry authorities. Data updates align with new policy releases or revisions, with no fixed cycle. Most documents are official formal papers with document numbers, effective dates, and clause hierarchies. Individual document lengths range from hundreds to tens of thousands of characters. Fields include policy numbers, applicable industrial directions, funding thresholds, approval processes, and more. Units are mostly billion yuan, ten thousand yuan, and similar.
What constraints do these characteristics impose on the "model access and configuration" link
The large variation in policy document lengths, including some very long documents, means context-related parameters must support long text processing. This prevents key clauses from being truncated. The timeliness and authority of policies require strict control over data source scope. It is necessary to confirm whether the model is allowed to call external networks for supplementary information, to prevent the introduction of non-official content. The structured feature of multiple fields requires matching corresponding fields during retrieval and recall. It is also necessary to configure filtering rules based on effective dates to ensure the timeliness of returned policies.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | State-owned industrial fund policy documents have large individual document lengths. This range accommodates complete content from multiple recalled policies and prevents key clauses from being truncated |
maxTokens | 4000–6000 token | Balances the completeness of model-generated content and response speed, and adapts to the long-text response requirements of policy Q&A |
quoteMaxToken | 1500–2000 characters | Limits the display length of a single quoted policy, ensuring quoted content in responses is clear and not redundant |
temperature | 0.1–0.3 | Policy Q&A requires rigor and accuracy. This range reduces random generation probability and avoids content that deviates from official basis |
AIPROXY_API_ENDPOINT | Fill in the corresponding node according to the deployment environment | Adapts to the access path of local deployment, ensuring normal model call link |
AIPROXY_API_TOKEN | Fill in the exclusive authorization key | Ensures the security of model calls and prevents unauthorized access |
Three common configuration mistakes
- Model calls return connection timeout errors, and logs show failure to access the specified API node. The cause is incorrect configuration of the
AIPROXY_API_ENDPOINTparameter, using an access address that does not match the deployment environment. - Model-generated policy interpretations include fictional policy document numbers or clause content. The cause is an excessively high
temperatureparameter value, leading to random generation that exceeds the rigor requirements of policy Q&A. - Responses do not return the expected json_object format, which is inconsistent with the output format of the official website experience environment. The cause is failure to configure the corresponding format output rules for the connected model, or failure to enable the model's format constraint switch.
How to confirm the configuration is complete
- Input "Central state-owned industrial fund establishment approval policies". Check if returned content accurately references official policy numbers, effective dates, and other fields, with no fictional information.
- View system logs to confirm that the
maxContextandmaxTokensparameter values in model call requests match the configured values, with no abnormal truncation prompts. - Adjust the
temperatureparameter to 0.2, and test whether the generated response remains rigorous, with no content deviating from official basis. - Verify that the response format of the connected model meets preset requirements, and is consistent with the output style of the official website experience environment.
The values listed above are common starting points, and should be evaluated against the user'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.