What the Data for This Category Looks Like
Ledger data for new energy development comes from multiple sources including project approval documents, grid connection acceptance reports, monthly power generation reports, and operation and maintenance inspection logs. Data update frequencies are categorized by document type: daily (real-time power generation data), weekly (operation and maintenance summaries), and monthly (ledger archiving). Individual ledger documents include fixed fields such as unique project ID, site longitude and latitude, installed capacity, grid connection time, monthly power generation, and abnormal operation and maintenance records. Installed capacity uses megawatts (MW) as its unit, while power generation uses megawatt-hours (MWh). Some documents contain both structured tables and unstructured handwritten inspection notes.
Constraints on Model Integration and Configuration
Multi-source heterogeneous document structures require models to adapt to both structured table and unstructured text parsing rules. This avoids missing field extraction or format errors. Fixed fields and units require generated summary reports to strictly match the units and field definitions of original data. Models must not generate content that deviates from the original material independently. Different update frequencies require distinct processing logic: real-time power generation data must load the latest entries first, while monthly archived data must fully load historical records. This means model context configurations must adapt to loading requirements for different data volumes. Some ledger documents exist in scanned format, requiring additional visual recognition capabilities. This increases the adaptation complexity of model integration.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | New energy development ledgers have large batch data volume, need to accommodate complete context of multiple monthly ledgers and operation and maintenance records to avoid truncating key information |
maxTokens | 2000–3000 token | A single ledger summary report needs to cover data statistics, anomaly analysis and conclusion suggestions; this value range covers the complete output length |
quoteMaxToken | 1500 token | Sufficient original ledger fields need to be retained for reference, ensuring each summary data point in the report can be traced back to the original document |
temperature | 0.1–0.3 | Ledger summarization requires maintaining data accuracy; a lower temperature parameter prevents the model from independently generating content deviating from the original data |
toolChoice | auto | Need to automatically select document parsing and data summarization tools based on the type of input data, adapting to multi-source heterogeneous new energy ledger data |
AIPROXY_API_ENDPOINT | Dedicated interface address for deployed models | Direct model calls to deployment nodes adapted for new energy data processing, ensuring interface routing and authentication rules match business requirements |
Three Common Mistakes
- After uploading a structured new energy ledger table, the model returns empty fields or a 400 error. The cause is failure to adapt the structured data parsing configuration, or failure to correctly associate file parsing routing rules during model integration.
- When calling an externally deployed Qwen model, basic conversation functions normally but uploading ledger files results in parsing failure. The cause is failure to configure the dedicated file parsing interface in
AIPROXY_API_ENDPOINT, or failure to correctly pass authentication parameters. - The data reference order in the generated ledger report is chaotic and does not match the project timeline of the original ledger. The cause is that the
maxContextconfiguration fails to retain sufficient context sorting information, or thetoolChoicesetting is incorrect, causing the sorting logic to fail.
How to Verify Correct Configuration
- Upload a single standard new energy ledger file, and check if the fields parsed by the model fully match the fields of the original document.
- Call the configured model interface, send a fixed ledger summarization request, and check if the units of the returned results are consistent with the original data.
- View model call logs in FastGPT v4.17.0, and confirm that the configurations of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENhave no format errors. - Adjust the
temperatureparameter to 0.5, compare the content of the two generated reports, and confirm that data accuracy is not significantly affected.
The values provided here are common starting points and should be evaluated against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.