What Data for This Category Looks Like
Daily documents for this category include establishment registration applications, articles of association, shareholder resolutions, residence certificates, business licenses, and food business license applications. Data comes from the full-process electronic enterprise registration system, the National Enterprise Credit Information Publicity System, the electronic business license system, and the residence information database. Data updates daily. Most documents use semi-structured formats, with fixed fields including entity name, residence address, shareholder information, and license type. Some documents have image attachments with handwritten signatures or seals, plus plain text content.
Constraints Imposed on Vector Models and Indexes
Data sources include multiple official business systems, so document formats exported from different systems vary. Index construction must adapt to parsing rules for multi-format text. Daily updated data volumes require an incremental indexing mechanism. This avoids excessive resource use from full index reconstruction. Documents include highly semantic vertical fields such as residence address, shareholder information, and license type. Vector models need strong adaptability to semantics in this specific scenario. Some documents include signature or seal attachments. The indexing link must support text extraction and vectorization for associated attachments.
Configuration Settings
All configurations below use FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxToken | 8192 token | Adapts to the average length of market entity registration documents, avoiding truncation of core application fields |
defaultToken | 2048 token | Acts as the default context window, covering the text length of common single registration materials |
| Chunk Length | 800–1200 characters | Ensures semantic completeness for single chunks of registration documents, avoiding splitting coherent content such as shareholder resolutions |
| Overlap Length | 100–200 characters | Maintains semantic connection between chunks, preventing fields like residence address and shareholder information from being split apart |
| Similarity Threshold | Calibrated via actual testing | Adapts to semantic matching standards for vertical fields, filtering irrelevant registration material entries |
| Single Search Limit | Top 10 results | Covers the retrieval scope for common associated registration materials, avoiding excessive redundant results |
Three Common Misconfigurations
- Symptom: When using the Alibaba Qwen text-embedding-v3 model, the interface shows no available channels under the default group. Cause: The channel mapping for the target model has not been configured in OneAPI, or group permissions have not been granted to the default group.
- Symptom: Knowledge base retrieval takes too long, eventually returning a timeout error. Cause: Chunk length is set too large, increasing single-chunk vector calculation time, or single search limit is set too high, leading to too many returned results.
- Symptom: Many irrelevant residence address or shareholder information entries appear in retrieval results. Cause: Similarity threshold is set too low, failing to filter documents with large semantic differences, or chunk overlap length is insufficient, causing semantic fragmentation.
How to Confirm Proper Configuration
- Run a vector model connectivity test. Confirm the target model appears in the interface-returned model list, with no error prompts.
- Upload a single establishment registration application. Confirm no format parsing failure records appear in the index generation log.
- Initiate a retrieval test. Enter the keyword "food business license application". Confirm the semantic matching degree of returned results meets business expectations.
- View incremental indexing task execution records. Confirm daily updated registration data has been successfully synchronized to the index database.
The values provided 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.