What data for this category looks like
Data for this category comes primarily from code repositories, dependency lists, license texts, and contribution records. Actively maintained documentation includes LICENSE files, NOTICE files, dependency license lists, and copyright notices. Each document includes fixed fields: license type, copyright holder, version number, and dependency name. Updates trigger with each version release or dependency change. A single update covers the compliance document set for one project or one dependency.
What constraints these characteristics impose on knowledge base retrieval and recall
Data sources and update rhythms require retrieval to support incremental synchronization and version isolation, preventing accidental recall of outdated compliance documents. Fixed document structure and concentrated technical terminology require retrieval to accurately match fields and terms, reducing interference from irrelevant content. Fixed update frequencies require the retrieval pipeline to quickly respond to data changes, ensuring recalled documents are always the latest version. Errors in compliance-related content directly affect commercial usage permissions for projects, so retrieval results must meet high standards for relevance and accuracy.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| similarity threshold | 0.75–0.85 | Match professional terminology and fixed formats of license texts to prevent recall of low-match irrelevant compliance documents |
| single search limit | Top 8–12 entries | Cover license information for multiple dependencies in a single consultation scenario, while keeping context length within the processing limit of FastGPT v4.17.0 |
| rerank switch | Enabled | Improve ranking priority for high-match license compatibility descriptions, preventing core compliance content from being obscured by low-relevance documents |
| query optimization switch | Enabled | Perform semantic expansion on user compliance inquiry questions, covering different license terminology expressions to expand effective recall scope |
| search mode | Exact match first | Adapt to fixed formats and professional terminology of license texts, reducing accidental recall of non-compliance content |
| designated dataset collection ID | Bound to a dedicated compliance knowledge base collection | Isolate license compliance data from other business data to prevent irrelevant content from interfering with retrieval results |
Three common mistakes
- Phenomenon: Retrieval results include large amounts of non-license-related code comments or project documents. Cause: No dedicated license compliance dataset collection is bound, so ordinary business content in the code repository is recalled.
- Phenomenon: Low retrieval accuracy, with messy and irrelevant matching results for inquiry questions. Cause: Compliance documents are not organized according to category requirements, and the expression formats of license types and copyright holders are not unified, leading to non-standard index splitting.
- Phenomenon: Too much redundant content is returned in a single retrieval, and core compliance documents are submerged. Cause: The single search limit is set too high, exceeding context carrying capacity, leading to irrelevant information interfering with retrieval results.
How to confirm configurations are correct
- View the dataset binding configuration, confirm that only compliance documents such as LICENSE files, NOTICE files, dependency license lists, and copyright notices are included.
- Submit a test inquiry such as "MIT license compatibility rules", check whether the match degree and relevance of returned results meet expected standards.
- Adjust the similarity threshold, verify whether changes in recall results under different thresholds meet the matching requirements of compliance documents.
- After enabling the rerank function, check whether high-match license compatibility documents appear at the top of retrieval results.
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.