What this type of data looks like
Sources of power industry research reports include research teams from securities firms covering the power industry, public reports from power industry associations, regular financial reports of listed power enterprises, and operation data announcements from grid companies, among others. Update frequencies vary widely. Regular securities research reports are released quarterly or monthly. Industry operation data is updated weekly or monthly. Corporate financial reports are released quarterly or annually.
Document structures typically include core business indicators such as installed capacity, power generation, and on-grid electricity prices, policy interpretations, and analysis of upstream and downstream industrial chains. Fields include publishing institution, release date, and investment rating. Units mostly use specialized power industry units such as ten thousand kilowatts, kilowatt-hour, and yuan/megawatt-hour.
What constraints do these characteristics impose on deployment and upgrade
The multi-source decentralized origins and differentiated update rhythms of power industry research reports require deployment to adapt to alignment processing for different data formats. This avoids retrieval errors caused by inconsistent units or fields.
The high proportion of long documents means the parsing process needs longer timeout periods and reasonable chunking strategies. This prevents core data from being truncated.
Frequently updated industry operation data requires support for incremental index updates. This avoids full index rebuilding and reduces computing resource overhead.
The upgrade link needs to synchronously update data source synchronization rules. This ensures newly added power policies and operation data can be included in the retrieval scope in a timely manner, while avoiding conflicts with regular research report indexing tasks.
How to configure parameters
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Power industry research reports have a large number of pages per document. The parsing process handles large numbers of tables and long text. Extending the timeout period avoids task interruption mid-process |
maxChunkSize | 1200–1500 characters | Power industry research reports contain professional industrial chain data and long sentence expressions. A moderate chunk length retains data relevance and avoids truncating core indicators |
RECALL_TOP_K | Top 10 results | The power industry covers multiple segments such as power generation, transmission, and distribution. A sufficient number of candidate documents must be retrieved to cover information from different links |
RERANK_TOP_N | Top 3 results | Focus on core relevant research reports. Filter redundant results to improve retrieval accuracy |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Some power industry research reports include complete industrial chain data attachments. This requires support for large file uploads |
INCREMENTAL_UPDATE_INTERVAL | 1 hour | Some high-frequency power operation data requires timely synchronization. Incremental updates reduce resource overhead of full index rebuilding |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three common errors
- Scenario: When parsing high-definition data charts from power industry research reports locally, the system reports
CUDA out of memory, and the task status shows failure. Cause: Power industry research reports contain large numbers of complex charts. The video memory usage requirements of the marker model are higher than those for general document parsing. Model loading parameters are not adjusted for the power industry scenario. - Scenario: After uploading a new version of a power industry research report, the existing image index is not automatically updated, and retrieval results still show old version charts. Cause: The
IMAGE_INDEX_AUTO_REFRESHconfiguration is not enabled. The used version does not support incremental updates for image indexes. Upgrade to the corresponding version and manually trigger a refresh. - Scenario: When using a locally privately deployed Qwen3-32B model to call the MCP MySQL query service, a
500 Internal Server Erroris returned. Logs show the model cannot parse structured query results. Cause: The local model is not configured with a tool call prompt template adapted to power industry data. There is a difference from the default prompt format of the online model, leading to tool call failure.
How to confirm correct configuration
- Upload a power industry research report containing high-definition data charts. Check whether the parsing task status shows success, with no timeout or memory-related errors.
- Manually trigger an incremental update task. Check that system logs only record index processing records for newly added research reports, with no prompts for full index rebuilding.
- Initiate a retrieval request. Verify that the number of returned results matches the configured value of
RERANK_TOP_N. - Call the test interface of the locally private model. Verify that the tool call process can normally return structured power industry data, with no format errors.
- Initiate concurrent request tests. Verify that the QPS carried by a single node meets business requirements. Confirm that multi-node horizontal scaling is supported to improve overall service capabilities.
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.