What the Data for This Category Looks Like
The data for this category primarily comes from enterprise OA systems, organizational structure databases, internal system documents, and user feedback tickets. Daily processed documents include process configuration sheets, approval node tables, form field descriptions, and change records. Data updates have no fixed cycle, are triggered immediately when business processes change, and occur at least once per month on average. Most data structures are structured tables and short text descriptions, containing core configuration information such as approval node permissions, field validation rules, and transfer paths.
What Constraints Do These Characteristics Impose on the Vector Model and Indexing Stage
The data characteristics of this category impose three constraints on the vector model and indexing stage. First, the document structure dominated by structured tables and short text requires that the chunking strategy balance the integrity of single-chunk content and semantic relevance, avoiding splitting the hierarchical associations of approval nodes. Second, the high-frequency update feature triggered by business changes requires the indexing update strategy to support incremental synchronization, avoiding performance losses caused by full reconstruction. Third, core configuration information mostly consists of permission and rule-based semantics, requiring the vector model to have accurate semantic matching capabilities, while also limiting chunk length to adapt to the context window limits of the model.
The following configurations are compatible with FastGPT v4.17.0:
How to Set the Configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-item content length of process configuration sheets and approval node tables, avoiding overly fragmented chunks or loss of key rules |
| Overlap Length | 100–200 characters | Retains the transfer logic association between approval nodes, preventing cross-chunk context breaks |
| Similarity Threshold | 0.75–0.85 | Matches the semantic similarity of approval rules and field descriptions, filtering low-relevance recall results |
| Single Search Limit | Top 10 entries | Covers common process configuration query scenarios, balancing recall efficiency and result volume |
maxToken | 8192 | Adapts to the context window of mainstream open-source embedding models, avoiding truncation of key configuration information |
defaultToken | 1000 | Adapts to the default context filling requirements of embedding models, optimizing the vector generation effect of short texts |
Three Common Misconfigurations
- Issue: When adding a new Qwen/Qwen3-Embedding-8B embedding model configuration, the existing configuration with the same name is automatically overwritten. Cause: The system uses the model name as the unique configuration identifier by default, and no grouping or rename management function is provided.
- Issue: After importing an Excel-format approval node table, a prompt indicating excessively long content appears when generating vectors. Cause: The chunk length was not adjusted for structured table data, and the default chunk parameters do not adapt to the maximum capacity of a single table chunk.
- Issue: When querying approval permission rules, a large number of irrelevant form field descriptions are included in the recall results. Cause: The similarity threshold was set too low, causing content with low semantic matching to be incorrectly recalled.
How to Confirm the Configuration Is Correct
- Import a single process configuration sheet, check that the number of generated chunks matches expectations, with no content truncation or excessive splitting.
- Submit a query containing an approval node name, verify the semantic relevance of the recall results, and confirm they fall within the preset similarity threshold range.
- Attempt to add a new embedding model configuration with the same name, confirm that the system prompts for duplicate configuration or retains the existing configuration, with no automatic overwriting behavior.
- Upload an Excel-format approval node table, verify that the length of the chunked data does not exceed the maximum range supported by the vector model.
The values provided 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 2026-10-19.