Urban Public Transport Ledger Summary and Report Generation: Knowledge Base Retrieval and Recall

What the data for this category looks like

Urban public transport ledger data mainly comes from on-board GPS terminals, card payment systems, and operation scheduling platforms. Full daily updates are performed, with real-time shift data synchronized every 15 minutes. A single ledger document includes fields such as route number, shift ID, departure time, actual arrival time, passenger count, operating mileage, and daily energy consumption. Field units are as follows: route number has no unit, passenger count is an integer, operating mileage is in kilometers, and energy consumption is in kilowatt-hours. Documents are stored in structured CSV or JSON format. The length of a single document is mostly between 200 and 500 characters.

Constraints on knowledge base retrieval and recall

Urban public transport ledger data includes multiple segmented fields. The update rhythm of real-time shift data differs from that of daily summary ledgers. When retrieving, it is necessary to distinguish the recall scope for real-time and historical data to avoid recalling expired data. The field formats of ledgers for different routes are unified, but data volume grows with the number of routes. The number of single recall results must be limited to avoid context overflow. Core retrieval keywords for ledger data are mostly precise identifiers such as route numbers and shift IDs. Fuzzy retrieval has low adaptability. The retrieval mode must be adjusted to match precise matching requirements, reducing invalid recall results.

Configuration Settings

Reference only; verify against the target FastGPT release and deployment documentation.

| limit | Top 10 entries | Controls the number of recall results to avoid exceeding the context processing limit of the large language model |

| searchMode | Precise matching | Adapts to the characteristic that the core retrieval keywords of ledgers are route numbers and shift IDs, improving recall accuracy |

| usingReRank | Enabled | Performs re-ranking on initial recall results, prioritizing content with higher matching degree to ledger fields |

| datasetSearchUsingExtensionQuery | Disabled | The ledger data structure is standardized, no additional query word expansion is required, avoiding introducing irrelevant results |

| collectionIds | ID list corresponding to the public transport ledger dataset | Limits the retrieval scope to only cover urban public transport operation ledger datasets, excluding other irrelevant data |

Three Common Configuration Errors

• Phenomenon: Retrieval results are empty, or return ledger data for non-target routes. Cause: collectionIds is not correctly configured to limit the retrieval scope, or the similarity threshold is set too low, introducing a large number of low-matching results.
• Phenomenon: Retrieval tasks time out. Cause: The limit value is too large, leading to too many recalled results exceeding the context processing limit, or the retrieval dataset scope is not limited, resulting in traversal of full-volume data.
• Phenomenon: Returned array format data cannot be directly used for ledger summary. Cause: usingReRank is not enabled, or the correct searchMode is not configured, leading to chaotic field order in recall results, which cannot be directly mapped to the format requirements of ledger summary.

How to Verify Correct Configuration

• Manually enter a single route number as the retrieval term, confirm that the returned route number matches the input, and no irrelevant data is included.
• View retrieval task logs to confirm that only the specified public transport ledger dataset is called, with no other irrelevant data sources involved.
• Adjust the similarity threshold to 0.8, compare the matching degree of recall results before and after the adjustment, and verify that the threshold adjustment effect meets expectations.
• Trigger a full-volume ledger summary task, confirm that the number of returned context contents matches the value set for limit.

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.