What data for this category looks like
The data for this category primarily originates from cloud billing statements, monitoring systems, business forecast reports, and stress test results, with a weekly or monthly update frequency. Daily generated documents include capacity planning reports, expansion application forms, resource utilization reports, and budget calculation sheets. Most document structures include fields such as resource identifiers, load thresholds, procurement cycles, and cost accounting items. Units cover computing cores, storage capacity, budget allocations, and similar metrics. No fixed, unified format template exists, and some documents include non-standard business annotation fields.
What constraints these characteristics impose on knowledge base retrieval and recall
Multi-source data requires the retrieval link to support cross-data-source associated recall, to prevent missing cross-system business data. The weekly or monthly update frequency means the knowledge base’s incremental synchronization cycle must match the business update rhythm, to avoid recalling outdated capacity planning data. Long, document-heavy structures require retrieval chunking to adapt to report text length, preventing splitting that disrupts business logic. Non-standard fields require retrieval to support custom field matching, to accurately locate resource-related business content. Additionally, the uniqueness of core business fields requires recall results to be strictly linked to exclusive documents for capacity planning and resource scaling, to avoid mixing in business data from other categories.
How to set configurations
The following configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72-0.85 | Match the business term matching degree of capacity planning documents, filter irrelevant low-relevance results |
limit | Top 10-15 results | Match the average length of capacity planning-style documents, avoid overly long contexts exceeding model processing limits |
searchMode | vector+keyword hybrid mode | Balance precise matching of structured fields and semantic recall of unstructured text, adapt to multiple types of business documents |
usingReRank | Enabled | Reorder recalled long document results, prioritize displaying core business content such as resource utilization and peak load |
datasetSearchUsingExtensionQuery | Enabled | Expand simplified user business terms to match professional expression variants in capacity planning scenarios |
collectionIds | Specify the collection IDs corresponding to capacity planning documents | Isolate business data, only recall knowledge base documents exclusive to this category |
Three common mistakes
- An embedding error is triggered when uploading capacity planning-style documents, with logs indicating abnormal field format. The cause is that capacity planning documents contain non-standard resource identifier fields, and format cleaning was not performed in advance.
- The number of retrieved results is far lower than the set
limitvalue. The cause is that thesimilaritythreshold is set too high, filtering low-similarity but relevant results that fit the business scenario. - Retrieval results mix non-capacity planning-style documents. The cause is that
collectionIdswere not specified, leading to recall of documents from other business collections.
How to verify proper configuration
- Upload a test capacity planning report, confirm that embedding completes normally with no format errors.
- Enter simulated capacity planning-related queries, confirm that the number of returned results falls within the range set by
limit. - Review retrieval logs, confirm that only documents associated with the specified
collectionIdsare recalled. - After enabling
usingReRank, confirm that result sorting prioritizes displaying core business content such as resource utilization and peak load.
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 2026-10-19.