What the Data for This Category Looks Like
Railway operation policy and regulatory basis data mainly comes from official regulations, industry standards, and internal operation specifications released by the national railway administration and national railway group. Data is updated in real time alongside new policy releases. Most documents are structured, containing fields such as policy document number, issuing authority, effective date, applicable scenarios, and specific clauses. Some documents include attachment tables and implementation rules. The length of individual documents varies widely, ranging from hundreds to tens of thousands of characters.
Constraints These Characteristics Impose on Knowledge Base Retrieval and Recall
Official policy data requires retrieval results to prioritize documents from authorized release channels, avoiding interference from non-official interpretations. Update frequency adjusts dynamically with policy launches, so knowledge base synchronization mechanisms must support on-demand incremental updates to prevent recalling expired policies. Wide variation in individual document length means long documents must be split to fit retrieval chunking rules. This avoids losing key clauses due to overly large chunks, or semantic fragmentation due to overly small chunks. The multi-field document structure requires retrieval to support precise filtering by fields such as policy document number and effective date, to narrow the recall scope.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.80 | Railway policy texts have high semantic similarity. A value too low will recall irrelevant policies, while a value too high will miss similar clauses. Calibrated based on actual testing |
limit | Top 8 entries | The number of railway policy documents is moderate. 8 entries can cover most relevant clauses, avoiding excessive results interfering with subsequent processing |
searchMode | hybrid | Combines keyword and semantic retrieval, adapting to the characteristics of policy documents that have both clear clause keywords and complex semantic logic |
usingReRank | Enabled | Railway policies have clauses with similar wording. Reranking can improve the ranking priority of highly relevant results |
datasetSearchUsingExtensionQuery | Enabled | Policy questions often contain industry-specific terms. Extended queries can cover synonyms and abbreviations, improving recall coverage |
collectionIds | Bound to the dedicated railway policy knowledge base collection | Distinguish policy documents from other categories, avoiding recalling irrelevant cross-industry content |
Three Common Misconfigurations
- Retrieval returns no results or only a small number of irrelevant contents. Cause: Failed to bind the dedicated
collectionIdscollection for railway policies, accidentally recalling knowledge base data from other categories. - The number of retrieval results is far lower than expected. Cause: The
similaritythreshold is set too high, causing the system to filter out a large number of relevant policy clauses that do not meet the threshold. - Cannot recall newly released policy content. Cause: No incremental synchronization mechanism configured, and policy documents in the knowledge base have not been updated in a timely manner.
How to Verify the Configuration Is Correct
- Navigate to the FastGPT knowledge base management page, check if the bound
collectionIdsis the dedicated railway policy collection. - Submit a test query containing a railway policy document number, verify that retrieval results prioritize official policy documents.
- Trigger an incremental synchronization task, confirm that newly released policy documents can be successfully imported into the knowledge base.
- Adjust the
similaritythreshold to 0.75, submit a test query, verify that the relevance of returned results meets expectations.
The values provided are common starting points and 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.