Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
This category of data originates from the national land spatial planning one-map system, project approval and supervision platform, land use status database, and ecological protection redline data. Update frequency is irregular. Daily processed documents include Land Use Pre-examination and Site Selection Opinion, Construction Land Planning Permit, Construction Engineering Planning Permit, redline drawings, and planning conditions. Fields include project number, land use scale, planning control requirements, redline coordinates, and others. The length of individual text documents varies widely, and the data volume of drawing files has no fixed standard.
What constraints these characteristics impose on the model access and configuration link
Data sources are scattered, and updates have no fixed cycle. This requires model access to support batch import and incremental synchronization of multi-source heterogeneous data. Documents include two categories: text and drawings, so multi-modal model capabilities must be configured. Fields are highly professional, so the model must accurately identify professional terms such as land use scale and redline conflicts. The data volume of individual documents varies greatly, so the context length processed in a single round must be reasonably limited to avoid exceeding the model's carrying limit.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length of multi-source documents in this category, avoiding exceeding the model's context carrying limit |
maxTokens | 2000–3000 token | Controls the output length of a single round of responses, matching the information density requirements of party building learning Q&A |
quoteMaxToken | 1500 token | Limits the total length of quoted document fragments, ensuring the relevance between quoted content and questions |
vision | Enabled | Adapts to the parsing requirements of drawing documents such as redline drawings and planning conditions |
temperature | 0.3–0.5 | Reduces the randomness of responses, ensuring the accuracy of professional government affairs and party building content |
AIPROXY_API_ENDPOINT | Fill in the exclusive address provided by the platform | Ensures correct routing of model requests, adapting to multi-model access scenarios |
Three common mistakes
- Symptom: An error
tls: failed to verify certificate: x509: certificate signed by unknown authorityoccurs when calling a third-party large model. Cause: Correct proxy certificates are not configured, or there are certificate verification restrictions in the network environment, preventing the model request from completing security verification. - Symptom: After uploading documents split by a local vector model to the server, retrieval results are abnormal or cannot match. Cause: The vector model used by the server does not match the embedding dimension of the local model, or key embedding vector information is lost during document format conversion.
- Symptom: After configuring model redirection rules, the actual called model does not match the expected one. Cause: The mapping relationship of redirection rules is not filled correctly, or the rule priority is configured incorrectly, causing the system to not call the target model as expected.
How to confirm the configuration is complete
- Initiate a test query, enter a party building-related land use pre-examination policy question, and check whether the response contains accurate professional terms and compliant content.
- Upload a redline drawing document, and check whether the model can correctly recognize the coordinates and planning control requirements in the drawing.
- View the model call log, confirm that the requested API address, key, and configuration items are consistent, and there are no certificate verification errors.
- Test the import and retrieval of multi-source documents, confirm that different types of documents can be correctly indexed and recalled.
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.