What this category of data looks like
Data sources include the full-process electronic enterprise registration system, the National Enterprise Credit Information Publicity System, the electronic business license system, and the residence information database. The update frequency is daily. Documents processed daily cover establishment registration applications, articles of association, shareholder resolutions, residence certificates, business licenses, food business license applications, and similar materials. Each single document contains fixed-format application fields, main entity information, attached proof materials, and similar content. There are no multilingual fields. Common standardized fields include natural identity numbers, unified social credit codes, addresses, and other standardized fields.
What constraints these characteristics impose on the vector model and index link
Daily updated data requires indexes to support incremental synchronization, to avoid resource consumption caused by full reconstruction. Documents of multiple types have large differences in length, so different chunking strategies must be adapted to retain key semantic information. Standardized identity and address fields require precise matching, so vector retrieval needs to optimize weights for these fields. The review link has extremely high accuracy requirements, so filtering rules for retrieval results must strictly adapt to business compliance requirements, to avoid misjudgments that lead to registration rejection or identity fraud risks.
How to set configurations
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of single declaration material texts, avoids truncation of key clauses |
| Overlap Length | 100–200 characters | Retains semantic connection between chunks, prevents logical breaks in long texts such as shareholder resolutions and articles of association |
| Similarity Threshold | 0.75–0.85 | Strictly matches the format and content of declaration materials, reduces misjudgment risks |
| Single Search Limit | Top 10 entries | Controls the verification burden for review personnel, focuses on the most relevant file materials |
maxToken | 8000 | Adapts to the context window requirements of the overall text of declaration materials, ensures complete loading of review content |
| Reranking | Enabled | Sorts initial retrieval results by semantic relevance, improves detection efficiency for key materials |
Three common mistakes
- Phenomenon: After configuring the vector model, retrieval requests still call the large language model. Cause: No independent vector model channel is specified in the knowledge base retrieval settings, and the large language model channel is mistakenly bound as the retrieval channel.
- Phenomenon: After importing the text data set, the status continuously shows indexing. Cause: No incremental update trigger rule is configured, or the
defaultTokensetting exceeds the model's supported upper limit, causing indexing tasks to block. - Phenomenon: The similarity value returned by retrieval exceeds the 0-1 range. Cause: The similarity output of the vector model is not normalized, or a non-standard similarity calculation method is incorrectly used.
How to confirm the configuration is correct
- The knowledge base configuration page of FastGPT v4.17.0 can be viewed to confirm that the retrieval channel is separately bound to the vector model channel, and is not confused with the large language model.
- A single small residence certificate document can be submitted, and the indexing task log can be checked to confirm that the task is completed within 10 minutes with no abnormal errors.
- Business keywords such as "shareholder resolution" and "residence address" can be entered, and whether the similarity values of the retrieval results are within the 0-1 range can be checked.
- The index update records can be viewed to confirm that incremental synchronization is automatically triggered daily, matching the daily update frequency of the data.
The values provided are common starting points and should be evaluated against the samples relevant to the specific use case.
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.