What the Data for This Category Looks Like
Data for internal office and employee services in railway operations comes primarily from internal dispatch logs, operation and maintenance manuals, internal Confluence documents, employee codes of conduct, and emergency response process libraries. Update rhythms vary significantly: dispatch logs are updated in real time, operation manuals are revised quarterly, and employee handbooks are updated annually. Most documents include dedicated fields such as section numbers, train identifiers, and response time limits. Units use railway-specific measurement standards including kilometers, minutes, and passenger trips. Individual document lengths vary widely, from hundreds of words of emergency alerts to tens of thousands of words of full-line operation and maintenance manuals.
What Constraints Do These Characteristics Impose on Knowledge Base Retrieval and Recall?
The diverse types and uneven update rhythms of internal railway operation data create multiple constraints for knowledge base retrieval and recall. Real-time dispatch logs require frequent synchronization, so the recall pipeline must support incremental updates and low-latency synchronization. The wide range of document lengths, mixing short emergency alerts and long operation and maintenance manuals, requires tailored chunking and recall weighting for different text lengths. Dedicated fields such as section numbers and train identifiers require support for field-level precise matching during retrieval, to prevent invalid recall across sections. External networks cannot parse Confluence documents hosted on internal networks, so internal data source synchronization rules must be configured to ensure internal documents can be properly imported into the knowledge base. High professional terminology density requires support for synonym expansion, to avoid retrieval omissions caused by differing expressions.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.72–0.80 | Adapt to the high density of professional terminology in railway operation documents. A value that is too low will introduce irrelevant section scheduling rules, while a value that is too high will miss emergency plans for similar trains. Calibrated based on actual testing |
limit | Top 8 entries | Railway operation documents are mostly categorized by section and train. Recalling 8 entries at a time can cover associated information for multiple trains on a single line, and avoid excessive results exceeding the context window |
searchMode | semantic | For professional operation and maintenance documents, semantic search can match implicit operational logic, covering scenarios that keyword search cannot, and adapting to scenario-based railway operation needs |
usingReRank | Enabled | Reorder recall results by relevance, correct sorting deviations caused by professional terminology synonyms in semantic search, and improve information accuracy in emergency scenarios |
datasetSearchUsingExtensionQuery | Enabled | Automatically expand synonyms related to railway operations, such as expanding "dispatch order" to "traffic command", to cover different expressions commonly used internally |
collectionIds | Configured by data source group | Differentiate between different datasets such as dispatch logs, operation and maintenance manuals, and employee handbooks, to avoid invalid recall across categories and improve retrieval accuracy |
Three Common Misconfigurations
- Empty results returned when calling the knowledge base retrieval API. Causes: The
similaritythreshold is set too high, filtering all qualifying railway-related documents, ordatasetSearchUsingExtensionQueryis not enabled, so internal common professional synonym expressions cannot be matched. - Failure to parse content when synchronizing internal Confluence pages. Causes: FastGPT internal network access permissions are not configured, or internal proxy rules are not set, resulting in failure to pull Confluence page data from the internal network environment.
- Errors when importing doc format documents or pushing data in batches. Causes: The corresponding parsing plugin is not enabled when importing doc format documents, or the submission does not follow the maximum limit of 200 groups per batch when pushing data in batches, mistakenly using the total push volume as the single-batch limit.
How to Confirm the Configuration Is Correct
- Submit a batch of test queries containing railway professional terminology, check whether the returned results include relevant documents for the corresponding sections and trains.
- View the knowledge base data source synchronization logs, confirm that the synchronization status of internal Confluence pages is successful, and that the content has been successfully imported into the knowledge base.
- Call the
pushDataAPI to submit 200 groups of data, confirm that the API returns a success flag, then submit 201 groups of data, confirm that a parameter error prompt is returned. - After enabling
usingReRank, compare the result sorting before and after reordering, confirm that the relevance ranking of professional terminology conforms to business logic.
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.