What This Category of Data Looks Like
Ledger data for urban investment platforms mainly comes from internal project management systems, financial accounting systems, and financing ledger systems. The core ledgers are divided into two types: project execution ledgers updated incrementally daily, and monthly summary ledgers updated by the 5th of each month. Documents primarily take the form of multi-page structured tables. Fields on each page include project number, project type, responsible entity, total budget, actual expenditure, completion milestone, and remarks. Most field units are ten thousand yuan, hundred million yuan, and calendar days. Some ledgers also include a small amount of unstructured project supplementary explanation text.
Constraints Imposed on Model Access and Configuration
The structured multi-field ledger format requires the model to accurately match field names and units. Model random output must be restricted. Monthly summary ledgers can reach several thousand characters per document. A sufficient context window must be configured to carry complete input data. The daily incremental synchronization feature requires the model to quickly distinguish new and old data. This avoids repeated processing of historical ledger content. Some ledgers include unstructured explanatory text. The model must handle mixed input of structured and unstructured data. Model call parameters that support both data types must be configured.
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 | Adapts to multi-page structured content of monthly summary ledgers, carries complete context information |
maxTokens | 2000–3000 token | Meets the generation length requirements for a single ledger summary report, avoids output truncation |
temperature | 0.1–0.3 | Reduces model divergence, ensures accuracy of field matching and unit recognition |
toolChoice | auto | Allows the model to independently choose whether to call tools to assist in ledger data summary calculation |
AIPROXY_API_ENDPOINT | Fill in the exclusive access address for the corresponding model provided by FastGPT official | Adapts to access paths for domestic models, avoids cross-region access delays |
AIPROXY_API_TOKEN | Bind the exclusive key for the corresponding model | Ensures valid identity verification for interface calls, prevents invalid token errors |
Three Common Mistakes
- Phenomenon: After configuring
AIPROXY_API_TOKEN, the interface displays an invalid token, and the interface returns a 401 status code. Cause: The token is not bound to the exclusive permission of the corresponding model, or extra spaces and line breaks are mixed in when pasting the key. - Phenomenon: Selecting the GPT-4o mini model and inputting "What about 350" triggers a 400 error, and the request works normally after switching to other models. Cause: The
maxTokensthreshold of this model is lower than the minimum input and output length of the current request, or the request format does not match the input structure required by the model. - Phenomenon: The generated summary report waits for all content to complete before returning at once, with no segmented streaming output. Cause: The streaming response configuration switch of FastGPT is not enabled, or the interface does not correctly adapt to the protocol requirements of streaming transmission.
How to Confirm the Configuration is Successful
- Submit test data containing more than 10 structured ledger fields, check that the interface returns a 200 status code, with no prompts for invalid tokens or format errors.
- Review the summary content returned by the model, confirm that the fields completely match the fields of the input ledger, and unit recognition is accurate.
- Test with a short test instruction, observe whether content is returned in streaming segments, and confirm that the streaming configuration takes effect.
- Submit full monthly ledger data, check that the generated report length falls within the configuration range of
maxTokens, with no output truncation.
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.