What this category’s data looks like
Data for power grid equipment yield rates and market trends comes from three sources: public trading data from provincial power trading centers, equipment ledger reports from grid operation and maintenance vendors, and power asset valuation databases from the financial sector. Data updates occur once daily, with full compilation of the previous day’s data completed in the early morning of the current day. Each document includes these fields: equipment model, daily grid-connected power, total operation and maintenance costs, per-unit equipment revenue, and operating hours. Corresponding units are: unit, kilowatt-hour, ten thousand yuan, yuan per unit, and hour. No additional derived statistical fields are included, and raw data is not normalized.
What constraints these characteristics impose on multi-turn dialogue and prompting
Daily full data updates require multi-turn dialogue to be limited to the current day’s data scope. This prevents cross-day data mixing that could lead to financial valuation errors. Since fields are not normalized, prompts must explicitly specify the unit and statistical scope of field values. This prevents the model from confusing revenue calculation logic across different equipment, which reduces financial report accuracy. Each document has moderate data volume but high field correlation. Multi-turn dialogue must retain context to track the specific equipment model the user is asking about. This avoids repeated requests for basic information that delays report broadcasting. Raw data has not been cleaned. Prompts must include data validation rules to filter abnormal power or revenue values, ensuring the rigor of financial broadcasting.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Power grid equipment data has high field correlation. Context information such as equipment model and statistical cycle must be retained across multi-turn dialogue to ensure the accuracy of financial broadcasting |
Recall count | Top 6–8 entries | Each daily report includes multiple sets of fields for multiple pieces of equipment. A sufficient number of recalled entries is needed to cover the dimensions of user inquiries |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Each daily power grid equipment report document contains ledger data for multiple pieces of equipment, which takes longer to parse |
UPLOAD_FILE_MAX_SIZE | 50 MB | Monthly aggregated power grid equipment data documents have large file sizes, so a reasonable upload limit must be allowed |
Similarity threshold | 0.75–0.85 | Low-correlation historical data must be filtered to prevent the model from confusing revenue data across different equipment |
Chat History Retention Rounds | Top 3–5 rounds | Only core queries and equipment information are retained in multi-turn dialogue to avoid redundant context that reduces broadcasting efficiency |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Issue: Multi-turn dialogue fails to recall data normally when using the qwen2.5-14b-int4 quantized model, while simple mode runs normally. Cause: Context window adaptation parameters for the model are not configured. The actual available context of the quantized model is smaller than the preset value.
- Issue: A 500 error is returned when importing a daily power grid equipment report document. Cause: The
UPLOAD_FILE_MAX_SIZEconfiguration is not adjusted, and the document size exceeds the platform’s allowed upload limit. - Issue: Power grid equipment yield rate data is truncated in multi-turn dialogue, and all fields for a single piece of equipment are not fully returned. Cause: The
maxContextsetting is too small, and cannot accommodate the total character count of context and document data, leading to incomplete financial broadcasting content.
How to confirm configurations are set correctly
- Upload a standard daily power grid equipment report document, check that the parsing status shows success with no error prompts, to ensure normal access to financial data.
- Launch multi-turn dialogue, ask for revenue data for different equipment models in sequence, confirm that context is not lost, and each follow-up question can associate with the correct equipment information to ensure the coherence of financial broadcasting.
- Adjust the value range of configuration items, verify whether the number of recall results meets expectations under different parameters, confirm that the
Similarity thresholdsetting can filter irrelevant data, and avoid incorrect associations in financial broadcasting. - Run a workflow that includes multiple AI dialogue modules. Check that the final output only retains the generation result of the last module, with no redundant content, to ensure the conciseness of financial broadcasting.
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.