What This Category’s Data Looks Like
This category of data originates from social insurance management systems, tax social insurance premium collection systems, market entity registration information, and shared public security and civil affairs data. The update frequency is daily. Daily processed documents include enrollment registration forms, payment base declaration forms, personnel change forms, social insurance premium verification forms, and personal contribution records. Data fields cover the unit's unified social credit code, personnel identity identifier, payment verification base, enrollment status, verification form number, and other items. Most data is exported as structured text or table formats, with relatively fixed per-record size.
Constraints for Vector Models and Indexes
Multi-source heterogeneous data sources require vector models to adapt to chunking across different document formats, and avoid damaging the integrity of key structured fields such as personnel identity and payment base. Daily update frequency requires index configurations to support incremental synchronization, and avoid excessive system resource usage from full index updates. Fields include precise identity and payment parameters, so vector recall must balance semantic matching and field association constraints, and prevent low-matching irrelevant data from entering internal office workflows. Batch structured documents require index shard configurations to support batch retrieval, and ensure response efficiency for internal office operations.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the structured paragraph length of documents such as enrollment registration forms and payment verification forms, and avoids damaging key field integrity through chunking |
| Overlap Length | 100–200 characters | Ensures semantic continuity of key information such as cross-chunk personnel identity and payment details |
| Similarity Threshold | 0.85–0.95 | Requires precise matching of personnel identity and payment parameters, and prevents low-matching irrelevant data from entering office workflows |
| Single Search Limit | Top 10 results | Adapts to the need for quick location of core documents in internal office work, and avoids excessive results increasing manual verification costs |
defaultToken | 2000 token | Adapts to the context length of a single batch of verification documents, and ensures complete retrieval results are brought into employee office workflows |
maxToken | 8000 token | Covers the stitching needs of multiple related documents, and meets context integration requirements for personnel changes and payment reconciliation |
Three Common Misconfigurations
- Phenomenon: Vector retrieval results do not include image-format enrollment vouchers, or images cannot be retrieved normally. Cause: Multi-modal vectorization and retrieval association settings are not configured. Only vector indexes are generated for text data, and image-format enrollment materials are not processed synchronously.
- Phenomenon: When repeatedly initiating personnel identity verification queries, the system triggers vector database retrieval each time, with no cache reuse. Cause: No query cache mechanism is configured. Repeated requests are not intercepted, increasing the load pressure on the vector database.
- Phenomenon: After calling the create training order interface, the enrollment data added to the collection does not generate a corresponding vector index. Cause: The processes of training orders and collection data addition are confused. Training orders are used for batch data vectorization scheduling, while collection addition only completes single data upload and does not trigger associated vectorization tasks.
How to Verify Correct Configuration
- View the index update log for FastGPT v4.17.0, confirm that the daily incremental synchronization task triggers normally with no failed records.
- Initiate two identical personnel identity query requests, confirm that the response time of the second request meets expectations, with no additional vector database call overhead.
- Upload an enrollment registration form and its corresponding payment verification form, verify that after creating a training order, collection data can normally generate a vector index and be retrieved.
- Upload an image-format enrollment voucher, verify that the retrieval result can normally return associated text and image data.
The values provided 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.