Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
Railway operation ledger data primarily comes from on-board operation monitoring systems, dispatching command platforms, ticket settlement systems, and line maintenance log repositories. Data update cycles cover three categories: real-time (on-board real-time operating status), daily (on-duty dispatching reports), and monthly (monthly operation summaries). Each individual ledger document includes structured fields such as train number, operating mileage, fault code, maintenance time, and energy consumption value. Units include kilometers, hours, kilowatt-hours, times, and others. The length of a single ledger text ranges from 300 to 1000 characters. Monthly summary reports can reach tens of thousands of characters.
What constraints these characteristics impose on the knowledge base retrieval and recall link
Railway operation ledgers have many structured fields and strict unit requirements. The retrieval process must match both semantic meaning and precise fields to avoid results with mixed units or misaligned fields. Real-time updated on-board data requires the knowledge base synchronization frequency to match the business update rhythm. Otherwise, recalled historical data cannot support current ledger aggregation. Long-document monthly summary reports require a reasonable chunking strategy to prevent context breaks that lead to incomplete retrieved segments. Railway operation ledgers involve compliance records. Retrieval results must be strictly limited to the exclusive dataset, and documents from other categories must not be included.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Railway ledger fields have strict requirements. A higher similarity threshold prevents irrelevant fault logs or ledgers for non-corresponding trains from appearing in results |
limit | Top 8 entries | Single ledger data has many fields. Too many recalled results exceed the workflow context window, disrupting subsequent aggregation and report generation |
searchMode | vector+keyword | Balances semantic association and precise field matching, adapting to the high number of structured fields in railway ledgers |
usingReRank | Enabled | After long documents are chunked, re-ranking filters the most relevant ledger segments, improving the content accuracy of summary reports |
datasetSearchUsingExtensionQuery | Enabled | Railway operations use many technical terms. Extended queries match ledger content with synonymous expressions |
collectionIds | Bind the exclusive railway operation ledger collection | Limits the retrieval scope, preventing documents from other categories from interfering with ledger aggregation and report generation |
Three common configuration errors
- Phenomenon: Retrieval results include non-railway operation documents, and fields do not match query requirements. Cause:
collectionIdsis not configured correctly, and the exclusive railway operation ledger knowledge base collection is not bound. - Phenomenon: The workflow calls the RAG node and returns empty results, with the interface showing an empty knowledge base associated ID. Cause: No valid
collectionIdsis specified in the configuration, so the system cannot locate the target dataset. - Phenomenon: The retrieval request times out, returning a 504 status code. Cause: The
limitparameter is not adjusted, and too many long document chunks are recalled at one time, exceeding the system processing threshold.
How to confirm the configuration is correct
- Go to the FastGPT knowledge base management page, check if the bound collection includes the exclusive railway operation ledger dataset, and confirm that the
collectionIdsconfiguration is correct. - Initiate a test query, enter "2024 May operation ledger for train G123", and check if the returned result fields include train number, operating mileage, and other railway operation-specific content.
- After enabling the
usingReRankfunction, compare the sorting of retrieval results before and after enabling to confirm that the re-ranking function operates correctly. - Run a workflow that includes RAG retrieval and tool calls, review the node logs, and confirm that the knowledge base recalled segments highly match the query content.
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.