What the data for this category looks like
Data for this category primarily comes from internal office systems, documents submitted to state-owned assets supervision platforms, annual operation ledgers, and project feasibility study archives. Update rhythm adjusts dynamically with business nodes: real-time addition of files during major project initiation stages, monthly updates for monthly operation analysis reports, and fixed-cycle releases for annual budget final accounts and annual reports. Most documents are multi-chapter official documents, including document numbers, issuing entities, enforcement clauses, and quantitative data items. Fields include project code, investment amount, approval nodes, and supervision requirement numbers. Common units are ten thousand yuan and month.
What constraints these characteristics impose on the "model access and configuration" link
Documents in this category mostly consist of long-chapter official documents, with individual documents of significant length and large volumes of structured quantitative data. Data sources are scattered, and update frequency adjusts dynamically with business nodes. This requires the model access link to support long-text processing capabilities to avoid content truncation caused by context overflow. The high proportion of structured data requires configuring retrieval logic matched to structured information to prevent failure of quantitative data matching. The characteristics of multi-source data and dynamic updates require configuring flexible data source synchronization rules to ensure knowledge base data aligns with current business status.
How to Configure Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Individual documents for state-owned capital investment companies are mostly long-chapter content. This range covers complete document context to avoid content truncation |
maxTokens | 2000–3000 token | Document drafting tasks require generating complete paragraph content. This range ensures the completeness of generated content |
quoteMaxToken | 1500–2000 token | The proportion of structured data and reference documents requiring retrieval is high. This range ensures effective display of retrieved content |
temperature | 0.3–0.6 | Official documents must remain rigorous and standardized. A lower temperature reduces randomness in generated content to ensure accurate expression |
AIPROXY_API_ENDPOINT | Exclusive domain name connected to the enterprise's internal AI service | Ensures data transmission security and compliance, meeting network security requirements of state-owned assets supervision |
AIPROXY_API_TOKEN | Exclusive token generated by the enterprise's internal permission system | Restricts unauthorized access to ensure API call security |
Three Common Configuration Errors
- Phenomenon: Knowledge base search responses time out, with the interface displaying a timeout prompt or status code 504. Cause: Context and retrieval configurations are not adjusted for long documents and structured data, causing the system to load excessive redundant data.
- Phenomenon: Generated documents contain professional term deviations or formats that do not meet industry specifications. Cause: No prompt template matching state-owned assets supervision requirements is configured, causing model output to not meet business scenario needs.
- Phenomenon: Vector retrieval results have low matching accuracy or slow retrieval speed. Cause: The selected embedding model has poor adaptability to structured data, or model deployment configurations are not adjusted based on local hardware resources.
How to Verify Proper Configuration
- Upload the longest individual internal document, test the model's context loading status, confirm there are no content truncation prompts, and adjust corresponding configurations based on test results.
- Input a drafting requirement containing structured data, verify whether the retrieval results include valid data for corresponding business fields, and adjust matching rules until expectations are met.
- Call the configured API interface, test the connectivity of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, and confirm there are no permission or connection errors. - Generate drafts of multiple different types of documents, compare the rigor of generated content, and adjust the
temperatureparameter to meet industry specification 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.