What Data for This Category Looks Like
Reference only; verify against the target FastGPT release and deployment documentation.
What Constraints These Characteristics Impose on Vector Models and Indexes
The annual update frequency means indexes do not need frequent incremental refreshes. A single full update involves a large total data volume. This requires support for multi-batch bulk imports. The mixed structure of structured forms and unstructured documents requires chunking strategies that balance field-level precise matching and paragraph-level semantic association. Data from multiple sources has format differences. This requires unified alignment rules for vector conversion. Fields with strong correlations between performance indicators and fund payments require indexes to prioritize associating corresponding data blocks. This avoids semantic drift.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale for This Setting |
|---|---|---|
| Chunk Length | 800–1200 characters | Ensures paragraph semantic integrity for performance reports and self-assessment forms, avoids splitting across semantic blocks |
| Overlap Length | 100–200 characters | Retains cross-block contextual association, matches cross-paragraph descriptions of performance indicators |
| Similarity Threshold | 0.75–0.85 | Filters low-match irrelevant supporting materials, accurately recalls content associated with performance targets |
| Single Search Limit | Top 8 entries | Controls token consumption per retrieval round, adapts to bulk association requirements for fiscal data |
maxToken | 16000 token | Limits total context length per round, prevents model timeouts under high traffic |
defaultToken | 4096 token | Sets basic retrieval context length, balances recall accuracy and query speed |
Three Common Mistakes
- After switching the knowledge base vector model, the interface progress bar stalls and the model cannot be reselected. Cause: The old vector index process was not terminated correctly, blocking the new index task.
- Passing more than 50,000 tokens in a single retrieval round causes delayed or timed out query responses. Cause: The upper limit of context tokens for single retrieval was not restricted, and non-essential data was sent directly to the model.
- Failing to enable vector normalization configuration leads to inconsistent matching degrees for recalled performance reports and self-assessment forms. Cause: The output format of non-normalized embedding models was not adapted, causing deviations in vector similarity calculations.
How to Confirm Configurations Are Set Correctly
- Enter the vector model settings interface. Confirm that normalization configuration is enabled for non-normalized embedding models.
- Run a small-batch import task for performance self-assessment forms. Check that the progress bar completes normally with no blocking errors.
- Search for the keyword "performance target completion status". Verify that the number and matching degree of recalled results match the preset threshold.
- Check the retrieval log. Confirm that single-round context token consumption does not exceed the preset value of
maxToken.
All configuration values provided here are common starting points. Adjust them based on specific samples and use cases relevant to the deployment environment.
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.