What data looks like for this category
This category's data primarily comes from the enterprise registration full-process electronic system, the national enterprise credit information publicity system, the electronic business license system, and the residence information database. The update frequency is daily. Daily processed documents include establishment registration applications, articles of association, shareholder resolutions, residence certificates, business licenses, food business license applications, and more. Structured documents contain fields such as unified social credit code, entity name, registration date, and licensing type. Unstructured documents are mostly signed and stamped scanned documents or electronic forms. The document structure primarily consists of application material collections for a single entity, with no fixed cross-entity aggregation format.
What constraints these characteristics impose on vector models and indexing
The daily updated data volume creates a need for incremental synchronization. Index construction must support incremental updates to avoid resource consumption from full synchronization. Documents contain both structured fields and unstructured attachments. Vector models must balance precise matching of structured text fields and semantic extraction of unstructured content. Chunking rules must avoid splitting core business fields. Market entity registration data relates to business access qualifications. The accuracy of index recall directly affects subsequent verification processes, so the number of single-recall results must be limited to avoid introducing irrelevant entries. Additionally, data from multiple sources must be unified in format before vector generation, otherwise semantic deviation will occur in embedding results.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average length of single documents for market entity registration, avoids semantic breaks, and adapts to the input limits of the maxToken parameter |
| Overlap Length | 100–150 characters | Ensures semantic coherence of core business fields across chunks, prevents critical content from being truncated during chunking |
defaultToken | 1024 | Balances semantic completeness of vector generation and computing resource consumption, adapts to the default embedding model parameters of FastGPT v4.17.0 |
maxToken | 2048 | Matches the input upper limit of mainstream embedding models, avoids semantic loss caused by truncation of long document content |
| Single Search Limit | Top 8 entries | Relevant results for market entity registration data are concentrated in a small number of entries; excessive recall will introduce irrelevant non-target entity data |
Three common configuration errors
- Phenomenon: Index tasks remain in the "Indexing" status for a long time and cannot switch to the ready status. Cause: No incremental update trigger rule is configured. When fully synchronizing daily updated market entity data, the excessive data volume causes index construction to time out.
- Phenomenon: The number of index entries in the dataset automatically increases, with a several-fold growth in a short period. Cause: The duplicate data deduplication switch is not enabled, or the chunking rule does not retain the integrity of core fields, causing the same material of the same entity to be chunked multiple times to generate indexes.
- Phenomenon: Index tasks using
text-embedding-3-largeget stuck. Restarting the service after commenting out the model configuration does not restore normal operation. Cause: The locally cached embedding model index files are not cleared, and the system fails to release old model loading resources, causing the model process to remain occupied after service startup.
How to confirm the configuration is complete
- Navigate to the dataset management page of FastGPT v4.17.0, check the index status of the market entity registration documents to be synchronized, and confirm that all entries show "Ready".
- Randomly select documents of different types such as establishment registration applications and food business license applications, perform a chunk preview operation, and confirm that the chunked content does not split core business fields such as unified social credit code and residence address.
- Trigger an incremental synchronization task, check the synchronization log, and confirm that only the data updated on the current day is included in the index, and no existing entries are regenerated.
- Input a test query containing a specific entity name or licensing type, check the recall results, and confirm that the ranking meets relevance expectations, with no irrelevant non-target entity data appearing.
The values provided here 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 2026-09-28.