What the data for this category looks like
The data source includes regulatory policies issued by the State-owned Assets Supervision and Administration Commission of the State Council, internal management systems of the parent group and its subordinate subsidiaries, and normative documents from local state-owned assets supervision and administration departments. Update frequency aligns with regulatory policy revisions, annual system tidying, and new business launch timelines. Most documents are structured official papers, with fixed fields including policy document numbers, issuing entities, effective dates, applicable scopes, and core clauses. Individual document lengths vary widely. Some attachments include segmented content such as tables and clause details.
What constraints these characteristics impose on the model access and configuration link
Policy documents’ structured nature requires model access to use precise recognition logic for key fields such as document numbers and effective dates, to avoid parsing deviations. Policy updates lack fixed cycles and demand high content rigor. Configurations must synchronize data sources with regulatory release rhythms to ensure Q&A content timeliness. Wide variance in individual document lengths requires adjusting context processing parameters to support long text parsing and prevent key regulatory clauses from being truncated. Policy content carries strong regulatory attributes. Configurations must lock compliant call scopes to block outputs that fail to meet state-owned assets supervision requirements.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long text length of state-owned capital policy documents, prevents key regulatory clauses from being truncated |
maxTokens | 4000 token | Controls the output length of a single round of Q&A, ensures complete presentation of policy clauses |
quoteMaxToken | 2000 token | Limits the length of referenced policy document fragments, ensures the accuracy of referenced content |
toolChoice | auto | Adapts to tool call requirements such as document retrieval and compliance verification that may be needed in policy Q&A |
temperature | 0.1–0.3 | Reduces output randomness, ensures the rigor and compliance of policy interpretations |
AIPROXY_API_TOKEN | Bind a dedicated call key | Restricts model call permissions, avoids compliance risks caused by unauthorized access |
Three common mistakes
- Phenomenon: When using SearXNG to access external retrieval, the number of returned results is insufficient or no matching content is found. Cause: The retrieval logic is not adjusted for the structured characteristics of state-owned capital policy documents, leading to non-critical fragments being incorrectly filtered.
- Phenomenon: When calling the deepseek-r1 model deployed via Ollama, the content within the <think></think> tags is empty. Cause: Full context transmission is not enabled in the model configuration, leading to the thinking process not being correctly captured.
- Phenomenon: Parsing errors occur after uploading state-owned capital policy PDF documents. Cause: Chunking parameters are not adjusted for official paper format PDFs, leading to parsing failures of structured content such as tables, headers and footers.
How to confirm the configuration is properly set
- Upload a single state-owned capital policy document, verify that the model can accurately extract preset key fields such as policy document numbers and effective dates.
- Submit test questions for policy clause interpretation, check whether the output content includes accurate policy reference fragments and contains no irrelevant statements.
- Trigger a tool call test, confirm that the model can correctly call the document retrieval tool to obtain the latest policy content.
- View background call logs, confirm that the token consumption of model calls does not exceed the ranges configured by
maxContextandmaxTokens.
The values listed above are common starting points, and should be evaluated 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.