What this category of data looks like
Data for this category comes from risk control systems, audit records, user behavior logs, and report evidence. Daily generated documents include ban records, appeal materials, review opinions, and user notifications. Data updates follow daily synchronization or real-time push cycles. Each data entry includes fields such as account ID, violation type, disposal basis, appeal content, and review result. Some associated evidence files require attached text or structured metadata. All data is linked by account. There is no universally fixed batch processing template for this data.
Constraints on vector models and indexing
Decentralized data sources with both structured and unstructured content require indexes to support multi-source data access and mixed-format vector extraction. Account-linked data requires indexes to aggregate by account ID, to prevent cross-account data confusion. Real-time or daily update cycles require support for incremental synchronization, to avoid long full index rebuild times that disrupt business response. Wide variation in information density across document types requires compatibility with vector generation for both short text (such as ban records) and long text (such as complete appeal materials), while preserving relationships between fields. High stakes for accurate account disposition require index recall precision to match review needs, to avoid incorrectly recalling data from unrelated accounts.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches typical single-paragraph text length for ban records and appeal materials, prevents critical information from being split across chunks |
| Overlap Length | 100–200 characters | Preserves contextual connections between chunks, prevents core appeal content from being split across index chunks |
defaultToken | 4096 | Matches input length limits for Qwen3-Embedding-8B, aligns with average token count for single appeal materials |
maxToken | 8192 | Supports long text processing during batch indexing, covers full text of review opinions and report evidence |
| Similarity Threshold | 0.75–0.85 | Balances false positive and false negative recall rates, matches precision requirements for account ban scenarios |
| Maximum Results per Recall | Top 10 entries | Limits the number of associated records returned per search, prevents excessive context from interfering with review judgments |
Three common configuration errors
- Symptom: Index status remains "Indexing" for an extended period with no progress updates. Cause: Incorrectly configured embedding model access address or secret key, or failed compatibility check between model version and FastGPT v4.17.0.
- Symptom: Existing configuration is overwritten after adding a new embedding model with the same name. Cause: Failed to configure unique identifiers for different models as required by the platform, and configuration items are identified only by model name.
- Symptom: Search results include ban records from unrelated accounts. Cause: Failed to configure index dimension filtering rules by account ID, leading to cross-account data confusion.
How to verify proper configuration
- Upload a single sample of ban records and appeal materials, view the vector preview after index generation, confirm text chunk splitting matches expected behavior.
- Initiate a simulated appeal review API call, check that returned results only link to records associated with the target account, with no cross-account data included.
- Review system logs, confirm that embedding model calls return normal results, with no timeout or format error prompts.
- Test updating a real-time generated appeal data entry, confirm that incremental index synchronization completes successfully, and the new data can be retrieved normally.
The values provided are common starting points, and should be evaluated against local test 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.