What the data for this category looks like
State-owned capital investment company ledger data originates primarily from state-owned assets supervision platforms, internal ERP systems, and monthly submission documents from affiliated enterprises. Two update modes are used: monthly batch updates, completed within 3 working days after the end of each month, and real-time updates that immediately synchronize progress changes for individual investments. Ledger documents use structured table format, with fields including project ID, investment entity, invested enterprise name, contributed capital (unit: ten thousand yuan), paid-in capital, supervision entity hierarchy, and project approval time. Some associated reports also include short text attachments for project feasibility studies.
What constraints these characteristics impose on model integration and configuration
Multi-source data format differences require configuring unified field mapping rules during model integration to avoid model recognition confusion caused by inconsistent source data. Monthly batch updates involve large data volumes; the context length of a single input may exceed the base configuration, so context parameters must be adjusted to cover complete ledger data. The rigor of ledger data requires limiting the model's randomness parameters to ensure accurate field output in model results. The need for fast generation of real-time update data requires that model call endpoints and permission configurations adapt to network access restrictions of state-owned assets systems.
How to set configurations
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers structured data from three monthly ledgers and associated report text, prevents context truncation that causes field loss |
maxTokens | 4000–6000 token | Limits the output length of a single round of report generation, meets standard length requirements for state-owned assets reports |
temperature | 0.1–0.3 | Maintains accuracy of structured field output, reduces generation of irrelevant information, and adapts to the rigor requirements of ledger data |
AIPROXY_API_ENDPOINT | Official proxy endpoint of the deployment region | Adapts to network access restrictions of state-owned assets systems, ensuring stability of model calls |
AIPROXY_API_TOKEN | Enterprise-specific key configuration | Complies with security requirements of state-owned assets supervision, preventing unauthorized model calls |
toolChoice | none | No external tool calls are required for ledger consolidation and report generation; generate results directly based on structured data |
Three common configuration errors
- Symptom: A 400 error triggers when calling GPT-4o mini with short text containing "350". Normal operation resumes after switching to another model. Cause: The permissions of
AIPROXY_API_TOKENin the model integration configuration do not cover the specific request rules of this model, or the proxy endpoint does not adapt to the input format requirements of this model. - Symptom: Only full content is returned when generating a report, with no streaming output effect. Cause: The streaming call setting for the corresponding model is not enabled in the model integration configuration, or the proxy endpoint does not support streaming transmission.
- Symptom: Reranker model call fails, unable to complete content reranking. Cause: The exclusive permissions of
AIPROXY_API_TOKENfor the reranker model are not configured correctly, or the proxy endpoint does not point to the correct path for the reranker model.
How to verify correct configuration
- Upload a single ledger test data entry containing "350", call GPT-4o mini, and check if a 400 error is triggered. If no error occurs, model permissions and endpoint configuration are normal.
- Enable the test generation function, observe whether returned content is pushed in batches, and confirm streaming output is working.
- Generate a monthly ledger consolidation report, check that units and values of all fields in the report are unified, and confirm the context is not truncated.
- Call the Reranker model for testing, check that reranked results are returned normally, and confirm token and permission configurations are correct.
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.