What the data for this use case looks like
Coal mine production ledger data sources include underground sensor collection systems, crew shift handover ledgers, equipment inspection logs, and safety inspection reports. Update frequencies fall into three categories: real-time (sensor operating parameters), after each shift (crew operation records), and daily summary (safety inspection reports).
Each ledger document contains fields such as operation location, equipment number, operating parameters, inspection results, responsible person, and timestamp. Equipment numbers use a 6-digit numeric format. Operating parameters include values with legal units of measurement such as pressure and temperature. Document length ranges from tens of characters to thousands of characters.
What constraints do these characteristics impose on the knowledge base retrieval and recall link
Real-time updated sensor data requires the knowledge base to support incremental synchronization, avoiding delays caused by full refreshes and ensuring that the latest equipment operation records can be retrieved.
Structured fields and fixed-format equipment numbers require retrieval to support exact match modes, avoiding irrelevant cross-equipment records introduced by fuzzy matching.
The wide variation in document length requires a chunking strategy adapted to ledger entries of different lengths, avoiding loss of context from overly short chunks or exceeding the model context window from overly long chunks.
Mixed storage of multi-source data requires isolating ledger data through a dedicated dataset to prevent interference from cross-scenario retrieval.
How to set the configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.72–0.85 | Coal mine ledgers have high requirements for field precision. A value that is too low will introduce irrelevant equipment records, while a value that is too high will miss associated records for the same equipment. This range is calibrated via actual testing |
limit | Top 8–12 entries | Each ledger is associated with many equipment records. Sufficient context is needed to support summary work, while avoiding exceeding the model context window |
searchMode | Exact Match Priority | Fields such as equipment numbers and operation locations in coal mine ledgers require exact recall, avoiding invalid results from fuzzy matching |
usingReRank | Enabled | Multi-dimensional ledger records need to be sorted by relevance to ensure that core equipment records are recalled first |
datasetSearchUsingExtensionQuery | Enabled | Ledger query requirements often include extended matching of equipment numbers, such as automatically associating number queries when entering equipment names |
collectionIds | Bind the dedicated coal mine production ledger dataset ID | Isolate knowledge base data from different business scenarios to avoid cross-scenario retrieval interference |
Three common misconfigurations
- Phenomenon: Retrieval returns results that match the configured parameters, but no content is output when generating a ledger report. Cause:
datasetSearchUsingExtensionQueryis not enabled, causing the model to fail to obtain extended associated ledger data, or the recalled documents are not correctly mapped to the fields required for the report. - Phenomenon: Retrieval works correctly in local testing, but retrieval results cannot be referenced from external release channels. Cause: The access permission of the ledger dataset is not configured as public, or the external release channel is not bound to the correct
collectionIdsparameter. - Phenomenon: Retrieval results include ledger records from non-target equipment. Cause: The
similaritythreshold is set too low, or the exact match mode is not enabled, causing fuzzy matching to introduce irrelevant data.
How to confirm that the configuration is complete
- Enter the dataset management page of FastGPT v4.17.0, check the configuration parameters of the dedicated ledger dataset, and confirm that parameters such as
similarityandlimitmatch the preset values. - Enter a query term containing an equipment number, perform retrieval, and verify that the equipment numbers of the returned results match the query term as expected.
- Switch to the external release channel, initiate a ledger summary request, and check whether the returned results include the retrieved ledger content, with no null values or missing fields.
- After enabling the
usingReRankfunction, perform retrieval again, and verify that the sorting of the returned results matches the business requirements for relevance.
The values provided 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.