What this category of data looks like
Data sources for thermal industry research reports include national energy regulatory agencies, heating statistics from local housing and urban-rural development departments, and survey data from professional energy consulting institutions. Update cadence follows monthly updates for basic operating data, quarterly updates for industry analysis documents, and annual updates for long-term planning content. Document structure includes abstract, core operating indicators, supply and demand analysis, and policy interpretation modules. Core fields include heating load, unit heating energy consumption, pipe network heat loss, and heating coverage service area. Corresponding units are megawatt, kilogram per gigajoule, gigajoule, and square kilometer.
What constraints these characteristics impose on model access and configuration
Thermal industry research reports contain a large number of professional energy terms and precise numerical indicators. Models must accurately associate indicators with analysis conclusions. This requires configuring domain-adapted context parameters and knowledge base association rules. Multiple data sources with different update frequencies coexist. Scheduling rules for incremental and full data pull tasks must be distinguished. Single research reports have a long length. Document segmentation and parsing timeout parameters must be adjusted to retain complete information. Industry data has strict unified indicator unit requirements. Field verification rules must be configured to ensure output matches original data.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | The core analysis sections of thermal industry research reports have a relatively long average length. Sufficient context must be reserved to retain indicator association logic |
recallTopK | Top 8–12 entries | The number of relevant research reports in the thermal industry is moderate. Too many recalls will introduce irrelevant analysis, while too few will miss key data |
rerankReturnCount | Top 3–5 entries | The reranking step must focus on the most relevant professional indicator analysis to avoid redundant information interfering with model inference |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Single long research reports require processing a large number of tables and text paragraphs. Sufficient parsing time must be reserved |
scheduleCron | 0 0 2 * * * | Adapts to the cadence of daily incremental synchronization of basic monthly update data and full update of quarterly research reports |
apiRequestTimeout | 60 seconds | Large models need to process professional term analysis and long context inference. This avoids request interruptions due to timeout |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: Calling a large model returns a
400 Bad Requesterror, with a prompt that the input text length exceeds the limit. Cause: ThemaxContextparameter is not adjusted based on the long document characteristics of thermal industry research reports, resulting in input content exceeding the context length supported by the model. - Phenomenon: After configuring a local reranking model, the interface shows a request error returning
Connection refused. Cause: The listening port of the reranking model is not opened in the Linux deployment environment, or the API address and port of the local model are not correctly filled in the FastGPT configuration. - Phenomenon: As concurrent requests rise, the system returns
MongoDB query timeout, and model response delay increases significantly. Cause: The concurrency limit of theapiRequestConcurrentLimitparameter is not configured, leading to exhaustion of the MongoDB connection pool and inability to process query and write requests in a timely manner.
How to Confirm Successful Configuration
- Upload a single thermal industry research report. Check whether the parsed text completely retains core indicators and analysis logic. Confirm that the parsing timeout configuration matches the document length.
- Initiate multiple consecutive retrieval requests. Observe whether timeouts or errors occur. Confirm that the request timeout and concurrency limit parameters match the current business load.
- Verify the trigger logs of the scheduled pull task. Confirm that the task synchronizes the latest research report data according to the expected cadence.
- Test the interface connectivity of the local reranking model. Confirm that the model address and port configured in FastGPT can be accessed normally.
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.