Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
Data sources include telecommunications base station construction drawings, on-site construction logs, core network equipment operation and maintenance ledgers, industry communication standards and specifications, and project qualification filing documents. Update rhythm adjusts with project progress: construction-related documents are updated with daily operations, while standard documents are updated per industry revision cycles. Document structure includes fields such as project number, construction site coordinates, equipment model, maintenance cycle, and consumable specifications. Units include meters, megahertz, man-hours, square meters, and others. Some documents contain unstructured content such as scanned drawings and handwritten records.
What constraints do these characteristics impose on the "knowledge base retrieval and recall" link
Individual documents for telecommunications engineering construction are lengthy. Construction logs and operation ledgers often contain multiple pages of details, which increases chunking and vectorization time. Retrieval requirements for precise fields like project number, construction site coordinates, and equipment model mean retrieved results must match specific attributes to avoid irrelevant content being included. Scattered data sources have large format differences, including CAD-exported PDFs, structured ledgers, handwritten scanned documents, and others, requiring a multi-format compatible document parsing process. Frequently updated on-site operation documents mean the knowledge base synchronization mechanism must support incremental updates to prevent retrieval results from lagging behind the latest construction progress.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.85 | Matches the precise attribute requirements of telecommunications engineering scenarios, filtering low-relevance non-target documents |
limit | Top 8–12 entries | Individual engineering documents have lengthy content; too many retrieved results will exceed the context window limit |
searchMode | Precise Match Mode | Prioritizes retrieving documents containing specific project numbers and equipment models, aligning with business retrieval needs |
usingReRank | Enabled | Long text chunks may have scattered semantics; the re-ranking mechanism can improve the relevance ranking of results |
datasetSearchUsingExtensionQuery | Enabled | Questions in construction scenarios often use colloquial expressions; extended queries can cover more relevant documents |
collectionIds | Configure exclusive collection IDs grouped by project | Data from different telecommunications engineering projects is isolated, avoiding content interference from cross-project retrieval |
Three common mistakes
- Phenomenon: Retrieval response time exceeds 15 seconds. Cause: The number of retrieved entries is set too high, and the re-ranking mechanism is not enabled. Vector calculation for long text chunks consumes excessive system resources.
- Phenomenon: Retrieval results include documents from non-current projects. Cause: Exclusive
collectionIdsare not configured for the current project, so content from cross-project datasets is retrieved simultaneously. - Phenomenon: Text from scanned construction logs cannot be effectively retrieved. Cause: An OCR preprocessing workflow adapted to scanned documents is not configured, resulting in incomplete original text extraction.
How to confirm the configuration is correct
- Access the FastGPT dataset management page, verify that the
collectionIdsassociated with the current project only include the specified project datasets, and exclude associations with irrelevant collections. - Submit a test query containing equipment models and construction site coordinates, verify that retrieved results only include relevant documents from the current project, and adjust the
similaritythreshold to match business precision requirements. - Submit a colloquial construction-related query, verify that relevant extended matching results are returned, and confirm that
datasetSearchUsingExtensionQueryis enabled. - View the retrieval log, verify that the response time for a single retrieval meets expectations, and adjust the
limitandusingReRankconfigurations to optimize overall latency.
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.