What This Category of Data Looks Like
Ledger data for state-owned capital operation companies is primarily sourced from the state-owned assets supervision and reporting system, internal financial accounting systems, and regular reports submitted by affiliated enterprises. Data updates follow a monthly core rhythm; quarterly special ledgers are updated as needed, and temporary regulatory ledgers are adjusted per regulatory requirements. Single ledger documents are in structured format, including fixed fields such as affiliated enterprise name, unified social credit code, total assets, operating revenue, and net profit. The primary unit is ten thousand yuan, and a single document contains multiple rows of data from dozens to hundreds of affiliated enterprises.
Constraints Imposed by These Characteristics on the Model Access and Configuration Link
The structured multi-field ledger format requires models to accurately match fixed fields to avoid parsing deviations. The large scale of multi-enterprise, multi-row data requires sufficient context capacity to prevent data truncation. The monthly/temporary update rhythm requires model access to support dynamic data source switching without reconfiguration each time. The strict standardization of state-owned assets data requires low randomness in model calls to ensure that summary results match original data. Additionally, field differences across different ledgers require models to adaptively identify them, so the configuration phase must adjust model parameters and calling logic for structured data.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the multi-field summary context of multi-enterprise ledgers, accommodates all affiliated enterprise data processed in a single batch to avoid content truncation |
maxTokens | 2000–3000 token | Controls the output length of a single report, matches the conventional length requirements of state-owned capital operation company ledger reports |
temperature | 0.1–0.3 | Maintains the accuracy of ledger data, reduces randomness of generated content, ensures summary results match original field values |
AIPROXY_API_ENDPOINT | Fill in the dedicated proxy endpoint according to the deployment environment | Adapts to network requirements for domestic model calls, ensures normal connectivity of model requests |
AIPROXY_API_TOKEN | Fill in the dedicated call token assigned by the platform | Completes identity verification for model access, ensures security and compliance of calls |
toolChoice | auto | Allows the model to automatically choose whether to call tools to process structured data, adapts to ledger field parsing and summary logic |
Three Common Errors
- Phenomenon: Low matching accuracy of vector recall results, core ledger fields are not correctly extracted. Cause: No corresponding vector model configured for standardized structured ledgers of state-owned capital operation companies, incorrectly using general unstructured document vector models.
- Phenomenon: Parameter incompatibility or network error prompts when calling the DeepSeek model. Cause: The
AIPROXY_API_ENDPOINTis not configured to adapt the proxy address of domestic models, and directly using overseas endpoints causes call failures. - Phenomenon: Fields are empty or parsed incorrectly after knowledge base file processing. Cause: No file processing model adapted for structured tables is selected, and general text processing models cannot recognize the fixed field structure of ledgers.
How to Confirm Proper Configuration
- Select complete ledger data of a single affiliated enterprise for testing, and check whether the generated summary report accurately matches all fields and corresponding values.
- View the model call logs in FastGPT v4.17.0, and confirm that requests for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENhave no identity verification or network error reports. - Adjust the
temperatureparameter to 0.2, run the same ledger data three consecutive times, and check the consistency of generated results to confirm that randomness control is effective. - Import batch ledger data containing more than 50 affiliated enterprises, and confirm that the
maxContextconfiguration is sufficient to accommodate all context, with no content truncation or incomplete output.
The values provided are common starting points and should be measured against the user’s own datasets.
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.