What Data for This Category Looks Like
Data sources for this category include online monitoring platforms, automatic pollution source monitoring data, ecological and environmental law enforcement systems, and reports submitted by third-party testing institutions. Daily generated documents include on-site inspection records, sampling records, monitoring reports, order for correction decisions, and administrative penalty decisions. Data updates occur daily. Data fields include monitoring values, on-site working condition descriptions, law enforcement document numbers, rectification requirements, sampling points, and other items, involving different measurement standards such as pollutant concentration, duration, and sampling volume. Some documents are unstructured text, and a small number of sampling records use image format.
Constraints These Characteristics Impose on the Model Access and Configuration Workflow
Multi-source heterogeneous data sources require model access to support parsing of different data formats, to avoid data format incompatibility issues. The daily update rhythm requires configuration of timed synchronization trigger logic, to ensure that ledger summarization obtains the latest online monitoring and law enforcement data. Unstructured on-site inspection records and sampling records require models to have strong context understanding capabilities, to accurately extract core fields such as law enforcement document numbers and rectification requirements. Sampling records in image format require models to support image parsing capabilities, to ensure complete collection of sampling data. In addition, fixed fields in law enforcement documents require that ledger content output by models must strictly match preset fields, to avoid information deviation.
How to Configure These Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Must accommodate the concatenated content of multiple on-site inspection records, sampling data, and law enforcement documents to avoid loss of core law enforcement information due to context truncation |
temperature | 0.1–0.3 | Ledger summarization and report generation require maintaining data accuracy and output rigor, and excessive divergence should be avoided |
AIPROXY_API_ENDPOINT | Locally deployed ollama/vllm service address | Adapt to compliance requirements for locally stored ecological and environmental field data, and prevent sensitive law enforcement data from being leaked |
AIPROXY_API_TOKEN | Exclusive token generated during deployment | Ensure the security of access permissions for model calls and prevent unauthorized access |
toolChoice | auto | Automatically select whether to call tools for structured data comparison, adapting to cross-verification needs of online monitoring data and on-site records |
vision | Enabled | Some sampling records use image format, and image content parsing is required to extract information such as sampling points and parameters |
Three Common Misconfiguration Issues
- Symptom: Calls to the ollama model return empty content. Cause: Port mapping for the ollama service is not configured correctly, or the local service is not started synchronously with the docker image, preventing FastGPT from establishing a valid connection.
- Symptom: Only the system default model is displayed, and external models cannot be added. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, causing the external model access entry to not activate. - Symptom: The model cannot parse content after an image is uploaded. Cause: The
visionparameter is not enabled in the model access configuration, preventing the image parsing capability from being called.
How to Confirm Successful Configuration
- Navigate to the model management page of FastGPT v4.17.0, and confirm that external models such as ollama and vllm are displayed in the available model list.
- Initiate a test call for a single on-site inspection record, and verify that the model can correctly extract core fields such as law enforcement document numbers and inspection points.
- Upload multiple sampling data and online monitoring data, and test whether the model can complete basic ledger summarization and concatenation.
- Upload sampling records in image format, and verify that the model can identify sampling parameter information within the images.
The values provided are common starting points and should be evaluated against applicable 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.