What this category of data looks like
This category of data mainly comes from the national basic catalog of government service items, administrative licensing item list, power and responsibility list, and local government service item databases, with a monthly update frequency. Daily processed documents include basic government service item catalogs, implementation lists, service guides, standard material catalogs, flowcharts, and frequently asked questions. Each item’s documents have a standardized field structure covering fixed items such as item name, acceptance conditions, application materials, and handling procedures.
What constraints do these characteristics impose on the vector models and indexing link
The standardized field structure of government item data requires vector chunking to retain field associations, avoiding cuts that damage critical business information such as acceptance conditions and application materials. The monthly update frequency requires indexes to support incremental synchronization, reducing resource consumption from full reconstruction. Documents include long-text service guides and structured material lists, requiring adaptation to mixed-format vector encoding. The party building Q&A scenario requires recall results to strictly match official government terminology, so the similarity threshold must be strictly controlled to prevent low-match content from interfering with Q&A accuracy.
How to set the configuration
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the conventional text length of a single government service item document, avoiding cutting critical fields such as acceptance conditions and application materials |
| Overlap Length | 50–100 characters | Retains contextual associations across chunks, ensuring complete expression of handling procedures and material lists |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Ensures recall results meet government terminology consistency requirements, balancing recall coverage and precision |
maxToken | 4096 token | Adapts to the long-context recall needs of party building Q&A, covering the information scope of cross-item associations |
| Reranking | Enabled | Filters low-correlation recall results, improving content precision for party building Q&A |
| Single Search Limit | Top 10 results | Controls the number of returned results, avoiding redundant information interfering with Q&A logic |
Three common mistakes
- Symptom: After uploading a service guide containing flowcharts to the knowledge base, image-related index results are empty, or no image indexing model configuration option appears in the interface. Cause: The local deployment version does not enable the image indexing plugin, or the corresponding model dependency packages are not loaded correctly.
Reference only; verify against the target FastGPT release and deployment documentation.
- Symptom: After enabling the reranking model, the results returned by the online recall test are not sorted by relevance, or low-match items are not filtered. Cause: The reranking model's call configuration is not linked to the current knowledge base's indexing link, or the similarity threshold is set too high, resulting in no valid input data for the reranking model.
How to confirm the configuration is correct
- Upload a single standardized government service guide document, view the chunked text fragments, and confirm that critical business fields are not cut.
- Launch a government Q&A test related to party building, check whether the source documents of the recall results are the uploaded government service item catalogs or guides.
- Adjust the similarity threshold, observe the change in the number of recall results, and confirm that the threshold configuration takes effect.
- Check the vector model call logs to confirm there are no connection errors or parameter exceptions.
The values provided here 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 2026-09-28.