What this category of data looks like
Data sources include territorial spatial basic information platform, natural resource survey and monitoring data, population and economic statistics data, and remote sensing images. The update frequency is annual cycle. Daily processed documents include territorial spatial overall plans, detailed plans, special plans, planning implementation evaluation reports, public opinion summaries, and others. Data fields cover spatial coordinates, constraint indicators, population and economic statistical values, remote sensing image metadata, and more. Single document length varies greatly, and multi-source data formats have obvious differences.
What constraints do these characteristics impose on the model access and configuration link
The annual update rhythm means there is no need to frequently refresh the knowledge base, but each update requires integrating multi-source heterogeneous data, which puts requirements on the model's context capacity. The large variation in document lengths requires adaptation to planning texts and spatial data of different lengths to avoid context overflow. The party building learning Q&A requires rigor and accuracy; model parameters need to balance content compliance and information accuracy, while ensuring that quoted planning document fragments can be traced back to avoid responses that conflict with actual planning.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapt to the total length of planning documents and evaluation reports, accommodate context information from multi-source data |
maxTokens | 4000–6000 token | Meet the response length requirements of party building Q&A, while avoiding timeout for single call |
temperature | 0.2–0.4 | Ensure the rigor of party building learning Q&A, reduce the divergence of responses |
quoteMaxToken | 1500–2000 token | Ensure that sufficient planning document fragments can be quoted as response basis |
AIPROXY_API_ENDPOINT | Access address of deployed internal proxy | Adapt to internal network environment, avoid security risks of public network calls |
AIPROXY_API_TOKEN | Key stored in environment variables | Avoid plaintext leakage of keys, comply with data security management requirements |
Three common mistakes
- Phenomenon: Directly calling the reranking model API returns parameter errors. Cause: The reranking model is not bound to the party building Q&A workflow in FastGPT application configuration. Calling the API alone cannot integrate it into the processing workflow.
- Phenomenon: GPT-4-turbo calls are automatically triggered at the hour, with no scheduled task configuration. Cause: FastGPT v4.17.0 enables hourly model cache refresh by default, and this configuration item has not been manually disabled.
- Phenomenon: Some PDF files have empty content after upload. Cause: The
visionparameter is not enabled. The image content of scanned PDFs is not recognized by OCR, and only plain text PDFs are supported.
How to confirm the configuration is complete
- On the model management page of FastGPT v4.17.0, test the configuration of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, and verify normal model responses are returned. - Upload planning documents and PDF files in different formats, confirm that all file contents are correctly parsed, and no empty content error occurs.
- Trigger a test request for party building Q&A, check whether the returned result quotes fragments of the planning document, and no prompt of no available channels is displayed.
- Adjust the
temperatureparameter to 0.3, test the rigor of Q&A, and confirm that the response meets the normative requirements of party building learning.
The values given 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.