What the data for this category looks like
The data source for personnel policy Q&A consists of internally officially released personnel management documents, including attendance and leave policies, compensation and benefit regulations, onboarding and offboarding workflows, performance evaluation rules, and more. Document update timelines follow adjustments to enterprise personnel policies, with immediate updates when new policies are released, and regular quarterly or annual reviews and revisions. Most documents use a hierarchical clause structure, containing fields such as policy number, effective date, applicable audience, specific implementation details, and more. Some documents include calculation-related units such as workdays, salary ratios, and leave days.
What constraints these characteristics impose on vector models and indexing
The hierarchical clause structure of personnel policy documents requires that chunking does not break clause integrity. Otherwise, semantic connection breaks will occur, reducing retrieval accuracy. Unfixed update frequencies and potential bulk additions require indexes to support incremental updates and bulk index construction, to avoid resource consumption from full rebuilds. Metadata fields such as effective date and applicable audience need to be filtered alongside vector retrieval, so indexes must support combined retrieval of metadata and vector similarity. Document lengths vary widely: short clauses are only tens of characters, while long clauses can reach thousands of characters. Chunked text must be controlled within the set maxToken range to avoid exceeding the embedding model’s input limits.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the common length of single personnel policy clauses, and avoids splitting complete clauses |
| Overlap Length | 100–200 characters | Retains contextual connections across chunks, and prevents clause logic from being disrupted by chunking |
defaultToken | 1024 token | Sets the default token base for chunks, adapting to the input requirements of basic embedding models |
maxToken | 4096 token | Adapts to the input upper limit of general embedding models, matching the token consumption of most policy clauses |
| Similarity Threshold | 0.75–0.85 | Filters low-match results, and focuses on core policy clauses |
| Single Search Limit | Top 10 results | Matches the common demand scope of employee inquiries, and avoids excessive redundant results |
| Reranking | Enabled | Corrects ranking biases from vector retrieval, and prioritizes the most matching policy content |
Three common errors
- Phenomenon: Unable to correctly import personnel policy documents of specified file types, and index creation steps get stuck. Cause: Parsing plugins for commonly used personnel policy document formats such as Word, PDF, and Markdown are not configured.
- Phenomenon: Knowledge base creation gets stuck during the index construction phase, with the interface showing timeouts or no progress. Cause: Chunk length is set too large, causing the single-batch processing data volume to exceed the system’s processing upper limit.
- Phenomenon: Search tests return empty results or errors, and only vector data is stored in the vector database without original documents. Cause: Original document storage configuration is not enabled, and only vectorized feature data was uploaded, making it impossible to associate with original policy clause content.
How to confirm configurations are set correctly
- Upload a single complete personnel policy document, and check if the chunked text fully retains clause hierarchy and core content, with no forced truncation or fragmented splitting.
- Launch a specific personnel policy inquiry, and check if the similarity scores of retrieved results fall within the preset threshold range, and core clauses are prioritized for display.
- View the stored content in the vector database, confirm that both original document text and vectorized vector data exist, and can be associated and matched via document ID.
- Upload a new personnel policy document, trigger incremental index construction, and check if the new document is automatically completed with vectorization and index updates.
The values provided here are common starting points, and should be evaluated 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.