What the data for this category looks like
Financial report data for the gas industry comes primarily from publicly disclosed periodic reports of listed gas enterprises, industry operation data released by local public utility regulators, and monthly statistical materials from industry associations. Update cycles include full quarterly, semi-annual, and annual financial reports, plus monthly core operation data. Common document fields include gas sales volume, revenue scale, gas source procurement cost, pipeline network operation and maintenance cost, number of residential and commercial users, and total length of transmission and distribution pipelines. Units are ten thousand cubic meters, ten thousand yuan, ten thousand yuan, ten thousand yuan, ten thousand households, and kilometers, respectively.
What constraints do these characteristics impose on model access and configuration
Gas industry financial report data has numerous detailed fields, with indicators across dimensions such as gas sales volume, user counts, and pipeline network length. The field coverage of monthly operation data differs from that of annual financial reports. This requires model access configuration to support adaptation to multiple data source formats. Some indicators have wide value ranges, alongside professional terms and unit annotations. This requires enabling term recognition and unit verification settings during configuration, to prevent the model from confusing statistical calibers. Monthly operation data is updated at high frequency. This requires the configured model calling interface to support high-frequency requests, with timeout parameters set appropriately to match the parsing time of large financial report documents.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Single gas financial report documents are typically long, requiring support for long text input |
UPLOAD_FILE_TIMEOUT_SECONDS | 600 seconds | Full financial report documents take extended time to upload and parse, preventing mid-process timeout interruptions |
enable_rag_terms_check | Enabled | Financial reports contain many professional terms and unit annotations, requiring verification of consistent indicator statistical calibers |
api_request_interval | 1–3 seconds | Matches the high-frequency update request rate of monthly data, avoiding interface current limiting |
embedding_batch_size | 16–32 | Gas financial reports have many fields; batch embedding improves parsing efficiency and supports vector storage for multi-dimensional indicators |
PARSE_FILE_MAX_SIZE | 500 MB | Supports upload and parsing of large annual financial report documents |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing the settings.
Three common configuration mistakes
- Phenomenon: When deploying a local Qwen3-30B-A3B model with VLLM, interface tests return connection timeout or parameter error prompts. Cause: The model's local API access address and port are not configured correctly, or the compatible adaptation mode for local models is not enabled in FastGPT.
- Phenomenon: After enabling multimodal image recognition, uploading images from gas financial reports results in a prompt that image content cannot be provided. Cause: The multimodal model deployed with VLLM is not correctly bound to the conversation node, or the model's image input support parameter is not enabled.
- Phenomenon: When configuring the Qwen3-Embedding-8B embedding model, test connections return an error that the vector field is empty. Cause: The embedding model's API return format does not match FastGPT's vector extraction requirements, or the model's vector output dimension parameter is not configured correctly.
How to confirm the configuration is complete
- Perform an interface test for the locally deployed model, compare the return results of directly calling the VLLM interface and the FastGPT calling interface to confirm content consistency.
- Upload a fragment of a gas financial report containing images, test the multimodal function to confirm the model can correctly recognize image content and generate relevant responses.
- Import a single gas operation indicator data set, test the embedding model's recall effect to confirm returned related documents have expected relevance to the input indicator.
- View the system log panel to confirm model request response status codes fall within normal ranges, with no frequent timeout or connection failure errors.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.