What Data for This Category Looks Like
Data for this category originates from four main sources: advertising platform backends, the National Enterprise Credit Information Publicity System, customer-submitted account opening application materials, and industry access rules. Processed daily documents include account opening application forms, scanned business licenses, matching industry qualification documents, and historical account opening audit records. Data updates follow two rhythms: daily batch synchronization, or updates triggered by individual account opening applications. Included fields are subject name, unified social credit code, advertising industry category, audit status, rectification requirements, and others. No uniform fixed unit attributes apply. Only some qualification documents have format requirements.
Constraints Imposed on Vector Models and Indexes by These Characteristics
Multi-source, multi-format data requires vector chunking to adapt to structured account opening application fields, semi-structured text from scanned qualification documents, and unstructured audit record content. Coexisting real-time updates triggered by individual applications and daily batch updates require indexes to support flexible switching between incremental updates and full reconstruction. Unique core identification fields such as subject name and unified social credit code require deduplication verification logic during index construction. Document lengths vary widely, from tens of characters for audit status to thousands of characters for industry rule text. Chunking parameters must adapt to text fragments of different lengths.
The following configurations apply to FastGPT v4.17.0.
How to Set Configurations
| Configuration Item | Recommended Setting Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to common text lengths of documents such as business licenses and industry qualifications, avoids overloading single chunks |
| Overlap Length | 100–200 characters | Ensures contextual coherence across chunks, covers cross-chunk associated information for qualification fields |
| Similarity Threshold | 0.75–0.85 | Matches the strictness of subject qualification verification, filters low-relevance historical audit records |
| Single Search Limit | Top 20 entries | Adapts to the volume of audit reference data for a single account opening application, avoids interference from redundant information |
maxToken | 4096 | Adapts to large model context windows, carries vector spliced content from multiple qualification documents |
defaultToken | Calibrated via actual testing | Adapts token conversion accuracy for documents of different formats, balances recall efficiency and accuracy |
Three Common Misconfigurations
- An interface timeout error occurs after configuring an external index model. The cause is failure to adjust
defaultTokento match the request limits of the external model, leading to single requests exceeding the token upper limit supported by the model. - Qualification field recall failure occurs after migrating from PGSQL to Zilliz. The cause is failure to retain the index mapping for the subject unified social credit code during migration, leading to failure of vector matching for core fields.
- No noticeable change to audit results after enabling the reranking model. The cause is failure to set a reasonable similarity threshold, leading the reranking model to only process a very small number of recalled results, which prevents observable optimization effects.
How to Verify Proper Configuration
- Upload a standard business license document, check that chunked text fragments fall within the set length range.
- Initiate a simulated account opening application retrieval, confirm that the number of returned historical audit records matches the single search limit configuration.
- View vector index update logs, verify that incremental updates triggered by real-time applications complete normally.
- Call the interface to test the
maxTokenparameter, confirm that spliced vector text does not exceed the maximum length supported by the model.
The values listed above are common starting points. They should be evaluated against samples from the actual 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 2026-10-19.