What this category of data looks like
Energy storage investment research data primarily comes from technical documents of power equipment manufacturers, public power grid dispatching ledgers, industry research reports, and grid connection policy documents. Data covers dimensions including cell performance parameters, station installed capacity, charge-discharge dispatching records, and energy storage system safety standards. Real-time data such as charge-discharge power and station operating status updates at minute-level intervals. Industry research reports and policy documents are updated weekly or quarterly. Document formats include dozens of pages of technical white papers, structured station ledger tables, and clause-based policy documents. Fields include exclusive parameters: installed capacity (unit: MW), cycle life (unit: times), charge-discharge efficiency (unit: %), and grid connection voltage level (unit: kV).
Constraints imposed on model access and configuration
Energy storage investment research data includes minute-level real-time operating data, dozens of pages of long-text technical documents, and structured ledger parameters. The high-frequency updates of real-time data require that model interface call latency be controlled within a reasonable range. Otherwise, real-time investment research analysis cannot be supported. Long-text technical white papers have a longer length than general industry documents. This requires adaptation to the model's context window length. Structured ledgers contain exclusive unit parameters. Enable field format verification in the vector model configuration to avoid unit confusion during embedding. The precise extraction requirement for clause-based policy documents also requires adjusting the recall strategy to match document structure characteristics.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | Individual energy storage technical white papers have relatively long lengths, requiring adaptation to long-text input limits |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing time for long-text technical documents is longer than that for general industry documents |
embedding_batch_size | 32–64 entries | Structured energy storage ledger data has a large volume, and batch processing can improve embedding efficiency |
Recall Count | Top 8–12 entries | Energy storage investment research needs to cover multi-dimensional parameters such as installed capacity, efficiency, and dispatching, requiring a sufficient recall volume |
Similarity Threshold | 0.75–0.85 | Structured parameter matching has high precision requirements, requiring filtering of low-correlation recall results |
rerank_top_n | Top 5 entries | Perform secondary screening on initial recall results to improve the accuracy of core parameter extraction |
The parameter values provided on this page are all conventional recommendations used to determine the starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing the configuration.
Three common mistakes
- Phenomenon: After switching the model channel, the frontend page returns a
304status code. Cause: The old interface configuration cached locally has not been refreshed, or the interface routing configuration has not been updated synchronously. - Phenomenon: When calling the model channel, the log shows "Data acquisition exception". Cause: The input field format of the vector model does not match the configured mapping rules, or the units of structured data have not been unified.
- Phenomenon: After deploying FastGPT in an intranet environment, the intranet-deployed model API cannot be called. Cause: The intranet port of the model API has not been opened, or the intranet proxy has not been configured to allow the server to access the model service.
How to confirm the configuration is complete
- Upload a typical energy storage technical white paper, check that the parsed text segments cover complete chapters with no truncation or lost content.
- Import a structured energy storage ledger, check that the field mapping after vector embedding is correct, and no abnormal conversion of unit parameters occurs.
- Initiate an investment research query, verify that the number of recall results matches the set similarity threshold, with no obvious irrelevant content.
- In an intranet environment, initiate a call through FastGPT's model testing tool, confirm that the interface connectivity and returned results are normal.
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.