What this category of data looks like
Product designers, interaction designers, and front-end product teams typically produce this category of data. The data includes interactive prototypes, page flowcharts, component descriptions, and review records. Teams source most data from Figma or Axure files, internal team design systems, user feedback, and usability test records. Teams usually update the data weekly or with each product version iteration. Documents exist in structured or semi-structured forms, with fields such as page hierarchy, operation paths, and component attributes. Some entries note details like pixel dimensions and interaction trigger methods. No uniform mandatory units apply.
What constraints these characteristics impose on knowledge base retrieval and recall
This category of data includes nested page hierarchies and interaction paths. A single block of text cannot carry complete logic. Retrieval and recall processes must associate contextual fragments to avoid losing operation link information after splitting. Data sources include vector prototype files and review records, with large format differences. Systems must use different parsing rules to extract valid fields. Update frequency is high. Retrieval and recall processes must ensure results match the current version of design documents to avoid using outdated interaction logic. Fields include detailed content. Systems must support multi-dimensional matching to cover various query needs of designers.
How to set configurations
This guide adapts to FastGPT v4.17.0. The following configuration items follow the knowledge base retrieval and recall logic of this version:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Match details of interaction paths and component descriptions, filter redundant content with low matching degrees |
limit | Top 6 entries | Cover the complete logic of page hierarchies and associated components, avoid excessive results interfering with judgment |
searchMode | Semantic retrieval | Adapt to contextual association of nested interaction paths, better aligns with business needs than keyword retrieval |
usingReRank | Enabled | Optimize the sorting priority of recall results, prioritize returning documents that fit interaction logic |
datasetSearchUsingExtensionQuery | Enabled | Perform semantic expansion on designers' fuzzy queries to match hidden document fields |
collectionIds | Bind exclusive prototype and design system collections | Limit retrieval scope to avoid mixing document data from other categories |
Three common mistakes
- Phenomenon: Knowledge base recall result accuracy is low, returning a large number of irrelevant review records or outdated prototype content. Cause: Teams do not limit the retrieval collection scope, or set the similarity threshold improperly, resulting in too many low-matching document fragments being recalled.
- Phenomenon: When uploading a 190MB Axure or Figma file, an "offset is out of bounds" error appears, or a system prompt indicates the request entity is too large. Cause: Teams do not adjust system default file parsing and indexing parameters. Single loaded file data exceeds system limits, causing out-of-bounds or capacity limit errors during indexing.
- Phenomenon: After enabling the knowledge base association function, answer generation speed slows significantly. Cause: Teams do not restrict the single search upper limit, or enable unnecessary re-ranking and query optimization, resulting in a significant increase in retrieval and recall time.
How to confirm the configuration is correct
- Bind the exclusive prototype and design system knowledge base collection, run a query containing interaction path keywords, and verify returned results only come from the target collection.
- After adjusting the similarity threshold, compare recall results under different thresholds to confirm matching degrees meet business requirements for detail accuracy.
- Upload a small test Axure or Figma file, verify no errors occur during parsing and indexing, and confirm the file upload and recall link works properly.
- After enabling query optimization and re-ranking functions, run a fuzzy query to confirm returned results sort logically to fit actual interaction needs.
The values provided here 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.