What data for this category looks like
Thermal power category financial report data mainly comes from enterprise internal thermal production operation ledgers, industry submission reports from energy regulatory authorities, and publicly disclosed annual or quarterly financial report documents. Data update rhythm follows two patterns: monthly operation data is updated by the 5th of each month. Quarterly financial reports are released within 15 working days after the end of the quarter. Annual financial reports are disclosed by April of the following year. Document structures include core fields such as total thermal supply, unit heating cost, coal consumption rate, pipe network loss rate, and revenue composition. Total thermal supply is measured in gigajoules (GJ). Unit heating cost is measured in yuan per gigajoule. Revenue items are measured in ten thousand yuan. The length of individual annual financial report documents varies widely. It is recommended to determine appropriate values based on internal sample statistics or actual testing.
Constraints imposed by these characteristics on model access and configuration
Thermal power financial report data has scattered sources, including internal operation data and publicly disclosed documents. The model access link must support unified docking and format conversion for multi-source data. Data fields contain a large number of energy industry-specific terms, such as coal consumption rate and pipe network heat loss rate. This requires the model’s semantic understanding ability to adapt to industrial domain text. The difference in update rhythms between monthly operation data and quarterly or annual financial reports requires the configuration link to match different data refresh cycles. Individual financial report documents are lengthy, and core indicators are scattered across multiple paragraphs. This requires the model’s context processing ability to adapt to long text analysis requirements.
How to set configurations
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
embedding_model | Select Alibaba-emb3 or a domain-specific professional embedding model | Thermal power financial reports contain a large number of industrial professional terms. This type of model delivers more stable semantic alignment effects for professional text |
chunk_size | 800–1200 characters | Professional paragraphs in thermal power financial reports are mostly 500–1000 characters long. Excessively long segmentation will split term associations. Excessively short segmentation will lose context logic |
rerank_top_k | Top 6–8 entries | Core indicators of thermal power financial reports are scattered across multiple paragraphs. A sufficient number of recall results are required before screening valid information |
api_timeout | 120–180 seconds | Batch processing of multiple periods of financial report data increases single interface request duration. This setting avoids task interruption due to timeout mid-process |
max_context_tokens | 8000–12000 | The effective text volume of a single thermal power annual report is approximately 6000–10000 tokens. Reserve sufficient space to accommodate context and historical conversations |
stream_response | Enable as needed. Models of the qwen3 category must be set to enabled | Some locally deployed large models only support streaming output. The corresponding calling mode must be matched |
The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to determine final values after actual testing on internal sample datasets.
Three common misconfigurations
- An index failure occurs when
embedding_modelis set toAlibaba-emb3. Cause: The API key and access address for this model have not been added on the model management page, or the service permission for the corresponding model has not been enabled. - A "This model only supports streaming" error is prompted after adding a locally deployed
qwen3model to the configuration. Cause: The native output mode of the model has not been matched, and a non-streaming call is used to initiate the request. - A
code500error is returned after accessingaiproxyvia axinference-deployedchatttsmodel, with the prompt "Cannot read p". Cause: The input parameter format of the speech model has not been correctly configured, or the proxy service has not forwarded the complete request body.
How to confirm successful configuration
- Test the embedding capability of
embedding_modelon the model management page. Enter a segment of professional text from a thermal power financial report, and check whether the returned vector values meet expectations. - Submit a small thermal power financial report document to test the configuration of
chunk_sizeandrerank_top_k, and check whether the recalled paragraphs cover core indicators. - Initiate a batch financial report analysis task, monitor the triggering of
api_timeout, and confirm that no timeout interruptions occur. - Call the model interface to test the
stream_responseconfiguration. Initiate a conversation for theqwen3model, and check whether streaming returned content can be received 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.