What the data for this category looks like
Data for this category centers on the full lifecycle of central enterprise investment projects. Daily documents include annual investment plans, project proposals, feasibility study reports, due diligence reports, asset appraisal reports, post-investment evaluation reports, and other related materials. Data is sourced from the state-owned assets supervision authority’s investment supervision system, enterprise ERP and project management systems, industry market data, audit reports, and financial statements. Updates occur on a quarterly basis. Data is grouped by individual project, and includes full-process associated information such as project approval, feasibility study demonstration, and post-investment evaluation. No unified fixed fields exist. Corresponding business information must be extracted based on each document type.
What constraints these characteristics impose on model access and configuration
Data for this category comes from multiple systems. Ensure model access data sources adapt to different permissions and formats to avoid cross-system data pull failures. The quarterly update frequency requires configured context windows to cover quarterly document collections, to avoid missing recently updated investment plans and post-evaluation data. Multiple document types have notable differences in token length. Set reasonable model context and output token limits to meet processing needs for different documents. Data aligns with state-owned assets supervision requirements. Model calls must comply with compliance configurations to ensure information transmission and storage meet state-owned assets management regulations.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers the total token count of multiple quarterly-updated investment project documents, to avoid truncating core project approval and post-evaluation content |
maxTokens | 2000–3000 token | Adapts to output summarization needs for long documents such as post-investment evaluation reports, to ensure complete output of analysis conclusions |
temperature | 0.3–0.5 | Maintains the rigor of party building learning Q&A content, to avoid generating divergent content that deviates from state-owned assets supervision requirements |
AIPROXY_API_ENDPOINT | Private cloud platform model proxy address | Adapts to model call links in private deployment scenarios, resolves inference differences between cloud models and the FastGPT platform |
AIPROXY_API_TOKEN | Valid access key obtained from the cloud platform | Completes model identity verification, to ensure call permissions are legal and compliant |
toolChoice | auto | Automatically matches required tools based on party building Q&A scenarios, no need to manually specify call rules |
Three common mistakes
- Symptom: A model provider tested successfully in OneAPI returns a 404 error when added in FastGPT. Cause: The model ID was not filled correctly, or the proxy address configured in
AIPROXY_API_ENDPOINTdoes not correctly map to the target model path. - Symptom: In private deployment scenarios, the inference speed of cloud models differs notably from the FastGPT platform. Cause: Reasonable
maxContextandmaxTokensparameters were not set, or the correctAIPROXY_API_ENDPOINTwas not configured, leading to increased link latency. - Symptom: The container runtime returns the error
Api response error: /api/core/ai/model/test?model=Doubao-l. Cause: The model name was not completed, or theAIPROXY_API_TOKENconfiguration is incorrect, leading to identity verification failure.
How to confirm the configuration is complete
- Run a model test on the model provider management page of the FastGPT platform, confirm that no errors are returned in the results, and the output content complies with the compliance requirements of party building learning Q&A.
- Import a single investment project proposal, launch a party building-related Q&A, and check whether the model's returned content covers the core information of the document, with no obvious truncation or missing content.
- Adjust the
temperatureparameter and verify the rigor of the generated content, to ensure it meets the party building learning requirements related to state-owned assets supervision. - Check the container runtime logs, confirm there are no network errors or identity verification failure prompts related to model calls.
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.