What the data for this category looks like
Coal-fired power integration ledger data comes from business systems including coal mining excavation, power generation operation, equipment maintenance, and fuel management. It includes fully mechanized mining face advance records, unit operation logs, coal quality test reports, equipment maintenance ledgers, and more. Update frequencies fall into three categories: daily (operation logs, inspection records), monthly (maintenance ledgers, fuel purchase ledgers), and quarterly (annual cost ledgers). Documents contain both structured fields and unstructured attachments. Structured fields include equipment number, power generation, and coal consumption. Corresponding units are megawatt-hours, tons, and hours. Unstructured attachments include on-site inspection photos, maintenance report PDFs, and more. Some ledgers include industry-specific fields such as fully mechanized mining face advance rate and unit load rate.
What constraints these characteristics impose on knowledge base retrieval and recall
Many structured fields have precise semantics. Retrieval must balance field matching and semantic matching to avoid the ambiguity of general retrieval. Unstructured attachments include images. The retrieval link must support image parsing. Otherwise, equipment identifiers and maintenance records in images cannot be covered. Data update frequencies vary. The recall strategy must support incremental synchronization to avoid resource waste from full retrieval. Single ledger documents are long. Chunking parameters must adapt to long texts to avoid truncating critical information. Many industry-specific terms are used. Retrieval must support term expansion to improve recall coverage.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.85 | Structured fields of coal power ledgers have high semantic precision. A too low threshold will mix in irrelevant equipment ledgers. A too high threshold will miss matching maintenance records. Adjust based on actual measurement calibration. |
limit | Top 8–12 entries | Single coal power ledger documents are long. Too many entries will exceed the context window. Too few entries will miss associated data for the same equipment across multiple time periods. |
searchMode | vector+keyword | Ledgers contain both structured fields and unstructured text. Hybrid retrieval balances precise field matching and semantic content matching. |
usingReRank | Enabled | Coal power ledger volumes are large, and similarity distributions are scattered. Reranking filters low-relevance redundant data across units. |
datasetSearchUsingExtensionQuery | Enabled | Ledger terms are mostly industry-specific. Extended queries supplement synonyms such as "unit load" and "power generation load" to improve recall coverage. |
collectionIds | Exclusive collection ID for the coal power business knowledge base | Isolate ledger data from different business lines to avoid mixing in non-coal-power irrelevant documents. |
Three Common Misconfiguration Issues
- Issue: An error occurs after adding a knowledge base in a local deployment. Logs show vector database import failure or timeout. Cause: The volume of a single coal power ledger document is large. Unadjusted chunking parameters cause the data volume to exceed the embedding batch limit of the local model during vector import.
- Issue: The proportion of retrieved content in the generated report does not meet preset requirements. Redundant data or critical information is missing. Cause: The
similaritythreshold andlimitparameter are not linked. Adjusting only one of them cannot precisely control the weight and quantity of recalled content. - Issue: The knowledge base cannot parse equipment inspection images in the ledgers. Only empty text content is extracted. Cause: The image parsing plugin is not enabled. Image data such as on-site photos and maintenance drawings in coal power ledgers are not converted into retrievable text information.
How to Confirm Configuration Correctness
- The operator accesses the knowledge base management page of FastGPT v4.17.0. The parsing status of uploaded coal power ledger documents is reviewed. All structured fields and images are confirmed to have completed parsing.
- A test query is initiated. The phrase "Unit 2 March maintenance ledger" is entered. The number of retrieval results is verified to match the
limitparameter setting. Similarity scores are confirmed to fall within the presetsimilarityrange. - The application debug mode is enabled. The retrieved context content is reviewed. Only coal power business-related ledger data is confirmed to be included, with no documents from other business lines mixed in.
- A new monthly maintenance ledger is uploaded. The incremental update function is tested. The system is confirmed to only synchronize new data, with no repeated recall of old documents.
The values provided are common starting points. They should be measured 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.