What the data for this category looks like
The data for this category primarily comes from creator backends, enterprise credit information platforms, account historical records, and contract systems. Daily processed documents include onboarding application forms, identity qualification scan copies, verification audit ledgers, and cooperation agreement texts. Data updates are triggered by application workflows. The dedicated data update frequency for a single application is per application, while batch data is synchronized daily. Data fields include account unique identifiers, qualification file numbers, audit statuses, cooperation periods, credit verification results, and more. The length of individual data entries varies significantly, ranging from tens of characters for short audit status fields to thousands of characters for full cooperation agreement texts.
How These Characteristics Impose Constraints on Vector Models and Indexing
This category’s data mixes structured metadata, unstructured text, and binary qualification attachments. This requires vector indexes to support both text embedding and precise metadata filtering. Data updates are triggered by application workflows, with batch data synchronized daily. Indexes must support incremental updates to avoid resource consumption from full reconstruction. The length of individual data entries varies significantly, so the system must adapt to different chunking needs for short status fields and long agreement texts. Additionally, data is tied to account access and identity credibility. The accuracy of vector recall directly affects verification accuracy, so parameter ranges for recall and ranking must be strictly constrained.
How to Set Configurations
The following configurations are formulated based on the interface and parameter rules of FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of qualification materials and agreement texts for this category, prevents semantic fragmentation from overly short chunks, and avoids excessive embedding computation load from overly long chunks |
| Overlap Length | 100–150 characters | Preserves semantic connections across chunks, adapts to mixed scenarios of short texts such as application forms and audit records, as well as long agreement texts |
| Similarity Threshold | 0.75–0.85 | Filters low-match irrelevant qualification or agreement data, prevents authentication errors caused by false matches, and aligns with the accuracy requirements for identity verification in this category |
| Single Search Limit | Top 10 entries | Controls the number of returned results, balances recall completeness and retrieval efficiency, and adapts to multi-source data verification needs for single applications |
maxToken | 4096 | Limits the maximum number of tokens for a single round of embedding, prevents embedding failure from exceeding the model’s supported upper limit, and adapts to processing of long-text agreement texts |
defaultToken | Calibrated based on actual measurements | Adapts to the default token allocation rules of different vector models, and is compatible with qualification documents and contract texts from multiple sources |
Three Common Misconfigurations
- Phenomenon: After selecting a custom vector database as the knowledge base, retrieval results are empty or a connection failure prompt pops up. Cause: The dedicated data source paths from the creator backend and contract system were not configured in the synchronization rules of the custom database, resulting in the index not loading business data for this category.
- Phenomenon: After enabling a specified vector model, embedding tasks frequently time out. Cause: A reasonable
maxTokenparameter was not set, and long-text cooperation agreements exceed the token limit supported by the model, causing requests to fail to complete normally. - Phenomenon: After enabling the indexing model, returned audit records do not match the current application. Cause: The metadata filtering function was not enabled, and the account unique identifier was not bound as a filter field, resulting in recall of irrelevant business data across applications.
How to Confirm Configurations Are Set Correctly
- Upload a standard onboarding application form and qualification materials, trigger an embedding task, and check that the embedding status displayed on the interface is successful.
- Input a test query such as "onboarding application record for a certain account", and verify that the returned results include dedicated fields for this category, such as audit status and cooperation period.
- Adjust the similarity threshold, observe changes in the number of returned results, and confirm that the threshold setting meets the matching accuracy requirements of the current business.
- View the incremental synchronization logs of the index, and confirm that update data triggered by applications has been correctly loaded into the vector database.
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 2026-10-19.