What the data for this category looks like
Internal office data for highway operations primarily comes from toll system logs, road patrol records, maintenance project ledgers, employee service manuals, toll policy documents, emergency response procedures, and similar sources. Daily patrol records and toll transaction logs are updated daily. Policy documents and large-scale maintenance project documents are released as needed.
Documents include both structured and unstructured types. Structured data mostly consists of tables with road segment numbers, toll rates, and patrol point IDs. Unstructured data covers multi-line patrol checklists and full emergency response procedures spanning dozens of pages. Fields and units include business-specific identifiers such as kilometers, hours, counts, and yuan.
What constraints these characteristics impose on knowledge base retrieval and recall
The high proportion of structured business data requires retrieval to accurately match dedicated fields such as road segment numbers and patrol points. This avoids broadly recalling irrelevant content.
The large difference in data update rhythms requires adapting to a hybrid synchronization mechanism. The mechanism supports daily incremental synchronization and on-demand full synchronization.
The wide span of document lengths, with both short and long texts, requires retrieval and recall to balance accurate matching of short texts and contextual association of long texts.
Employees often initiate queries using colloquial expressions, such as "Which highway segment has patrols today". Converting colloquial language to standard business terminology improves recall matching accuracy.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.65–0.75 | Highway operation business data mostly consists of structured fields and precise business scenarios. This setting filters irrelevant recall results with low matching accuracy |
limit | Top 8–12 entries | In internal office scenarios, employee queries mostly target single scenario needs. Too many recall results will exceed the context window limit |
searchMode | vector+keyword | Simultaneously covers keyword matching for structured fields and semantic recall for unstructured text. This adapts to mixed-type business documents |
usingReRank | Enabled | There are many long and mixed documents. Reranking optimizes the relevance ranking of recall results. This improves the accuracy of employee queries |
datasetSearchUsingExtensionQuery | Enabled | Employees often use colloquial expressions for queries. Query optimization converts colloquial language to standard business terminology. This improves recall matching accuracy |
collectionIds | Bind three dedicated dataset IDs for toll operations, road patrols, and employee services | Isolates knowledge base data from different business modules. This avoids recalling irrelevant content and improves retrieval efficiency |
Three common errors
- Symptom: After upgrading the version or migrating the database, the knowledge base page displays complete documents, but the corresponding content cannot be recalled during conversation calls. Cause: The vector index was not regenerated, or the
collectionIdsof the target dataset were not correctly bound. - Symptom: After a single document is split, the number of chunks exceeds 3000, and retrieval recall results are missing. Cause: Ultra-long documents were not split. FastGPT has a default limit on the number of chunks per document. Chunks exceeding the threshold cannot be correctly indexed.
- Symptom: The retrieved results do not return matching image content. Cause: Image OCR and vector embedding configurations were not enabled, or image files were not uploaded to the corresponding business knowledge base collection.
How to confirm the configuration is complete
- Enter the FastGPT knowledge base management page. Check the vector index status of the corresponding business dataset, and confirm that all documents have completed indexing.
- Initiate a simulated employee query, such as "XX segment's today's patrol schedule". Check whether the number and matching degree of recall results meet the configuration requirements.
- Test a colloquial query, such as "Which parts of the highway need maintenance". Confirm that correct content corresponding to the business scenario can be recalled.
- Upload a patrol point image, initiate a corresponding query. Confirm that business content related to the image can be recalled.
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.