What the data for this category looks like
Power grid equipment research reports primarily come from power industry research institutions, public bidding announcements of grid enterprises, technical white papers of equipment manufacturers, and regular periodic reports of listed companies. Update cadences include regular quarterly and semi-annual industry analyses, as well as ad-hoc updates when grid bidding policies are released or core equipment technologies are iterated. Document structure centers on structured parameter tables, with fields such as equipment model, rated voltage, rated capacity, insulation grade, paired with long-form content including technical selection analysis and market supply and demand interpretations. Field units mostly follow power industry standards such as kV, MVA, Ω, etc.
What constraints do these characteristics impose on model access and configuration
The multi-source mixed nature of power grid equipment research reports requires the model access link to adapt to parsing rules for different data formats, requiring targeted document splitting and structured extraction parameter configuration. The large number of structured parameter tables and standard unit fields requires the model to preserve unit consistency during context processing, avoiding unit conversion errors. The high-frequency nature of ad-hoc updates requires data source synchronization configuration to support on-demand triggering, adapting to the rapid response needs of temporary updates. The mixed structure of long-form and structured content requires the model's context window configuration to balance semantic understanding of unstructured text and accurate extraction of structured parameters, avoiding truncation of key technical parameters due to an overly small window.
How to configure parameters
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | Power grid equipment research reports include long-form analysis and multiple sets of structured parameters, requiring sufficient context to cover complete technical logic and parameter associations |
chunkSize | 1000–1500 characters | The structured tables and text paragraphs of power grid equipment research reports have balanced lengths. This segmentation preserves the integrity of parameters within tables while avoiding excessive single segment length impacting recall accuracy |
recallTopK | Top 8–12 results | Power grid equipment research reports cover highly specialized subfields, requiring sufficient recall coverage of content across different technical dimensions to avoid missing key parameters or market analyses |
similarityThreshold | 0.75–0.85 | Technical parameters for power grid equipment have strong industry standardization. This threshold filters low-relevance general text while retaining accurately matched research report content |
apiRequestTimeout | 600 seconds | Parsing some large power grid equipment research reports takes significant time. A sufficiently long timeout avoids losing complete content due to parsing interruptions |
parseStructuredTable | Enabled | Power grid equipment research reports include a large number of standardized parameter tables. Enabling this setting improves the accuracy of parameter extraction |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Symptom: After enabling aiproxy, calling an Ollama model returns empty results. Cause: Ollama's model interface has incorrect cross-origin configuration or port mapping, preventing FastGPT from fully receiving streaming data returned by the model.
- Symptom: After configuring an external model API Key, the new model does not appear in the model list. Cause: The FastGPT service has not been restarted, or the model cache has not been refreshed in the model management interface, causing the system to fail to synchronize and load the external model configuration.
- Symptom: A 504 timeout error occurs after a workflow calls an external model. Cause: The time required to parse long-form power grid equipment research reports exceeds the configured
apiRequestTimeoutvalue, causing the request to be interrupted by the gateway.
How to verify successful configuration
- Enter the FastGPT model management interface and confirm that the added external models appear in the available model list.
- Upload a single typical power grid equipment research report, trigger model retrieval and Q&A, and verify whether the returned results include core technical parameters and corresponding analysis content from the research report.
- Test calls to different types of external models and confirm that the return format of each model meets the workflow configuration requirements.
- View system logs and confirm that there are no error records for failed model calls, and that request latency falls within the configured timeout threshold.
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.