What the data for this category looks like
Data for this category primarily originates from government service declaration portals, internal approval management systems, and archived file repositories. Data updates align with individual declaration batches or review workflows, with no fixed cycle. A single submission includes structured declaration forms and unstructured attachments. Structured fields include the declaration entity’s unified social credit code, project number, declaration amount, submission time, and other relevant fields. Most attachments are PDF-format qualification certificates, commitment letters, and similar documents. Individual document lengths vary widely, ranging from hundreds of characters to dozens of pages.
How These Data Characteristics Constrain Model Access and Configuration
The mixed data structure of structured forms and unstructured attachments requires models to support both structured field extraction and unstructured text parsing. Parameters such as maxContext must be explicitly configured to adapt to documents of varying lengths. Data updates follow no fixed cycle and vary significantly across batches, so dynamically accessed document sources must be supported, and static caching strategies avoided. The wide range of individual document lengths requires limiting the maximum context range for single processing to prevent model call timeouts. Different fields have dedicated units and format requirements, so extraction rules must be clearly defined in the configuration to reduce format parsing deviations.
How to Configure the Parameters
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the mixed text length of individual declaration materials and case files, avoiding exceeding the model’s context capacity limit |
maxTokens | 2000–3000 token | Limits the output length of a single review result, ensuring case review conclusions are clear and complete |
quoteMaxToken | 1500 token | Restricts the character range of referenced original materials, preventing redundant content from interfering with review judgments |
toolChoice | auto | Allows the model to independently choose whether to call tools to assist in extracting structured fields, adapting to mixed document scenarios |
temperature | 0.1–0.3 | Reduces generation randomness, ensuring consistency in review field extraction and format, meeting the strict requirements of government documents |
AIPROXY_API_ENDPOINT | Fill in according to the deployed model service address | Connects to locally or cloud-deployed large model services, ensuring compliance of data transmission paths |
Three Common Configuration Mistakes
- Symptom: Model calls return empty results or 500 status codes. Cause: Local model access parameters are not correctly configured in the
.env.localfile, causing the platform to fail to recognize the deployed model service. - Symptom: Timeout errors occur during batch case review. Cause: The
maxContextparameter is not adjusted to adapt to individual document lengths, causing single request load to exceed the model’s capacity limit. - Symptom: Extracted declaration field content lacks necessary separators, leading to parsing failure. Cause: The
temperatureparameter is not set to a low range, so the model-generated text does not retain reasonable grammatical separation formatting.
How to Verify Successful Configuration
- Run a local model connectivity test to confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly, and that test results meet expectations. - Upload a single typical declaration material, verify that the field extraction results output by the model match the original document content, and adjust parameters until the format matches.
- Initiate a batch test task, observe the success rate and time consumption of batch processing, and adjust
maxContextandmaxTokensparameters to adapt to concurrency requirements. - Check system logs to confirm there are no records of model call timeouts or parameter parsing errors, verifying configuration stability.
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.