What this category of data looks like
This category of data mainly comes from compliance-related materials generated during game development and distribution, including game story scripts, text lines, art resource descriptions, user agreements, daily produced content self-check lists, sensitive content modification records, and compliance review minutes. Data updates occur alongside game version iterations or event launch schedules, with a single update covering all compliance-related documents for a single version. Most documents combine structured text and unstructured descriptions, with no unified fixed fields. Corresponding content must be extracted based on compliance scenarios.
What constraints these characteristics impose on the vector models and indexes link
Game content compliance self-check data updates are triggered by versions or events, with a single update covering all compliance materials. This requires indexes to support incremental updates and batch synchronization. The data includes multiple types of content such as plot texts and art descriptions, so vector models need to adapt to semantic extraction of long texts and unstructured descriptions. Compliance documents have no unified fields, so support for custom metadata association is required. Additionally, due to the timeliness requirements of audits, the retrieval link must ensure low latency. Furthermore, compliance data involves regulatory requirements and sensitive content, so the security of vector storage and indexing processes must be ensured to avoid sensitive information leakage.
How to set configurations
The following configurations are formulated based on the interface and parameter logic of FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Ensures single-segment semantic integrity for game story scripts and compliance documents, avoids semantic fragmentation caused by excessive splitting |
| Overlap Length | 100–200 characters | Ensures semantic connection between adjacent chunks, covers cross-chunk compliance keyword associations |
maxToken | 4096 token | Meets long-text vector extraction requirements for compliance documents, matches the context window of mainstream embedding models |
defaultToken | 1024 token | Used for basic vector generation of short-text compliance items, balances computing resources and semantic coverage |
| Maximum Search Results | Top 10 results | Meets the rapid review requirements of compliance self-checks, avoids excessive results increasing audit burden |
| Similarity Threshold | Calibrated based on actual testing | Needs to be adjusted based on the sensitive word matching accuracy of compliance scenarios, balances recall rate and false positive rate |
Three Common Misconfigurations
- Symptom: The embedding model prompts that no available channel is found, or vector synchronization fails. Cause: The access permissions for the model's affiliated group are not correctly configured, or the required API key format is not filled in correctly.
- Symptom: The number of retrieval results does not match expectations, or the rearrangement link takes too long. Cause: Unreasonable chunk length settings lead to excessive chunks, or the similarity threshold is set too low, resulting in a large number of irrelevant content being recalled.
- Symptom: Corresponding compliance document metadata cannot be associated after vector generation. Cause: Metadata synchronization configuration is not enabled, or custom compliance-related fields are not correctly filled in during document import.
How to Confirm Configuration is Correct
- Import a single compliance self-check list document, check whether the vector generation progress completes normally with no error prompts.
- Initiate a compliance keyword retrieval, verify that the number of returned result chunks matches the set chunk length.
- Adjust the similarity threshold, compare the number of sensitive content recalls in retrieval results to confirm it meets scene requirements.
- Test the incremental update function, import a new sensitive content modification record, confirm that the index automatically completes synchronization.
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.