What data for this category looks like
Declaration materials and file data for infrastructure construction mainly come from official archived files across project approval, construction, and acceptance stages. Sources include the construction permit approval system, project budget management platform, and completion acceptance archive library.
Data updates are pushed in batches according to project progress. The approval phase updates declaration forms and qualification certificates. The construction phase updates progress logs and supervision reports. The acceptance phase updates final accounts and acceptance opinions.
Most documents are multi-page PDFs, mixing structured tables and long text descriptions. Fields include project ID, construction unit qualification level, budget amount (ten thousand yuan), acceptance date, and more. Field units are fixed and must strictly follow official specifications.
What constraints do these characteristics impose on the model access and configuration link
The above data characteristics impose clear constraints on model access and configuration.
Multi-page long documents and mixed formats require models to support long context loading and multi-format parsing. The maxContext parameter must adapt to the total length of a single material.
Batch-updated data requires knowledge bases to support incremental synchronization, and must match the compliance configuration of the proxy address.
Fixed fields and units require models to strictly follow preset field rules, avoiding outputs that do not meet official specifications.
Multi-material associated review requires the context to hold multiple related files, so limit the number of materials loaded at one time and total token consumption.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Single infrastructure declaration file often contains thousands of words of multi-page materials, and needs to hold context content for at least 3 related files |
maxTokens | 4000–6000 token | Review results need detailed annotations and compliance conclusions, so sufficient output length must be guaranteed |
temperature | 0.1–0.3 | Review work requires rigor and consistency, avoiding irrelevant or subjective content generated by the model |
quoteMaxToken | 2000–3000 token | Force the model to only reference file segments within the specified length as review basis, avoiding mixing irrelevant content |
AIPROXY_API_ENDPOINT | Enter the deployed private proxy address | Meet data compliance transmission requirements for public sector and state-owned enterprise projects, preventing sensitive data from leaking |
toolChoice | auto | Automatically trigger knowledge base retrieval functions to cross-check declaration materials and historical files |
Three common misconfigurations
- Phenomenon: A
context length exceedederror is returned when calling the model. Cause: ThemaxContextparameter was not adjusted for long declaration files, exceeding the model's preset context limit. - Phenomenon: The model output includes irrelevant content without citations. Cause: The
temperaturevalue is too high, or thequoteMaxTokenconfiguration is not set to limit the range of knowledge base segments referenced. - Phenomenon: Vector configuration errors occur when deploying a specified model. Cause: The vector service address corresponding to the model was not matched, or
AIPROXY_API_TOKENwas not correctly configured for authentication.
How to verify the configuration is complete
- Upload a single declaration file exceeding 8000 tokens, check whether the actual usage value of
maxContextin the FastGPT v4.17.0 call log matches the configured value. - Submit a file review request, verify that the file segments referenced in the output content only include content within the configured
quoteMaxTokenrange. - Switch between different model channels, test whether
AIPROXY_API_ENDPOINTcan connect normally and return valid review results. - Submit a file containing specific fields, confirm whether the model output strictly matches the preset field units and format requirements.
The values provided here are common starting points and should be evaluated 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.