What data looks like for this category
Typical subjects are product designers, interaction designers, and front-end product teams. Daily processed documents include interaction prototypes, page flowcharts, component specifications, and review records. Data sources primarily come from Figma or Axure files, design systems, user feedback, and usability test records. Update frequency is usually weekly or during each version iteration. Document structures mostly include page hierarchies, operation paths, component attributes, and modification comments. Fields cover page IDs, operation nodes, component names, modification times, and more. Some files have nested structures with no unified standardized field format.
Constraints imposed on vector models and indexing workflows
Nested interaction prototype and review record structures require retaining contextual links during chunking. Splitting must not break operation path integrity. Frequent version updates require index configurations that support incremental synchronization. This reduces resource costs from full index rebuilds. Data includes extensive operation path descriptions and abnormal scenario notes. Vector models must adapt to long-text contextual associations. Indexes must cover frequently modified content. Data volume grows with each version iteration. Processing scale for single-batch indexing must be limited to avoid excessive resource usage. The maxToken parameter must be set to match the large model’s input limit. This prevents critical operation descriptions from being truncated during vectorization.
How to set configurations
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches single-page content length of interaction prototypes and review records, avoids splitting that breaks operation path context |
| Overlap Length | 100–200 characters | Retains operation path links between adjacent chunks, reduces context breakage risk |
maxToken | 4096 token | Matches large model input limits, prevents critical operation descriptions from being truncated during vector generation |
defaultToken | Calibrated via actual testing | Matches maximum input length of local vector models, ensures complete content of prototype files is correctly vectorized |
| Single Recall Limit | Top 8 results | Matches core focus range of front-end teams’ daily reviews and modifications, reduces redundant retrieval results |
| Maximum Context | 3000–4000 characters | Matches single-round Q&A length of interaction design documents, ensures retrieval results cover complete operation paths |
Three common configuration mistakes
- Phenomenon: Vectorization tasks trigger rate limit errors. Logs show request frequency has exceeded limits. Cause: Parallel processing parameters were not adjusted. Thread count is set too high, causing vectorization requests to exceed interface quotas.
- Phenomenon: Knowledge base retrieval responses take a long time. Performance is poor under the same model configuration. Cause: Chunk length is set too large, leading to excessive calculation load for single-batch vector retrieval. Retrieval recall filtering rules are not optimized.
- Phenomenon: Q&A pair extraction stays in indexing status for extended periods. It cannot switch to ready status. Cause: Incremental update trigger logic is misconfigured, or source file synchronization permissions are abnormal. This prevents index synchronization tasks from completing.
How to confirm configurations are correct
- A small interaction prototype file may be uploaded. Vectorization task logs may be reviewed to confirm that chunk length and overlap length match configured requirements.
- A retrieval targeting operation paths may be initiated. The number of returned results may be verified to match the configured single recall limit.
- An incremental update task may be submitted. Index synchronization progress may be reviewed to confirm that the task completes within a reasonable time and switches to ready status.
- Duplicate entries in the dataset may be identified. Incremental update deduplication rules may be verified to be active, with no extra duplicate indexes generated.
The values provided are common starting points. They should be evaluated against the specific samples used in the deployment.
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.