What This Category of Data Looks Like
This category of data comes primarily from game operation backends, player segmentation systems, payment data reports, and activity operation logs. Daily produced documents include activity execution plans, reward configuration tables, pre-launch checklists, and activity post-mortem reports. Data updates follow weekly cycles, or temporary adjustments ahead of holiday events. Document fields typically include activity unique identifiers, reward types and values, effective time ranges, and trigger conditions. Some configuration items have clear units, such as reward distribution limits and player trigger thresholds.
Constraints on Vector Models and Indexes
The mixed structured and semi-structured nature of this category’s data requires vector indexes to support both tabular structured reward configuration tables and long-text activity plans. High-frequency data updates require indexes to support incremental synchronization. Full reindexing would cause delays that harm activity launch efficiency. Activity configuration fields have clear units and numerical constraints. Vector models must retain semantic associations between fields to avoid retrieval bias from lost numerical information. Activity execution has extremely narrow error tolerance margins. Retrieval result accuracy directly impacts configuration verification efficiency. Index recall precision must align with strict business validation requirements.
How to Set Configurations
This configuration is based on the interface logic of FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Aligns with paragraph lengths of activity plans and row counts of reward configuration tables, avoiding cross-chunk splitting of critical configuration items |
| Overlap Length | 100–200 characters | Preserves critical context such as cross-chunk activity timelines and reward rules, preventing semantic integrity loss from chunking |
| Similarity Threshold | 0.75–0.85 | Matches strict activity configuration validation requirements, filters low-relevance historical activity plans, and reduces manual verification costs |
maxToken | 4000 | Supports long-text retrieval of activity post-mortem reports, ensuring complete recall of associated configuration details |
| Single Search Limit | Top 5 entries | Matches standard verification scopes for operation personnel, avoiding excessive results that increase screening burden |
defaultToken | Calibrated via actual testing | Adapts to token limits of different vector models, preventing retrieval failures caused by token overflow |
Three Common Configuration Errors
- After switching the knowledge base vector model, the interface shows no synchronization progress, and reverting to the original model is not possible. The cause is failure to clear the index cache of the old model, so the system remains bound to the original model’s index path.
- When configuring the Doubao embedding model, testing returns "404 page not found". The cause is failure to correctly fill in the model’s dedicated API endpoint, or failure to enable permissions for the corresponding model in channel configuration.
- Vector normalization configuration is not enabled. When using an unnormalized vector model, similarity ranking of retrieval results deviates. The cause is that vector model outputs are not normalized, leading to inconsistent similarity calculation baselines.
How to Verify Successful Configuration
- Upload a test activity configuration table and historical activity plans, run a retrieval, and check if the similarity ranking of returned results matches expectations.
- Test the vector generation interface on the model channel configuration page, confirm that returned vector data has no abnormal errors, and complies with the set limits of
defaultTokenandmaxToken. - Manually trigger an incremental index synchronization, wait for the task to complete, and check the index update log to confirm that newly uploaded activity documents have been correctly indexed.
- Adjust the similarity threshold to the boundary of its range, verify that the number of retrieved results adjusts reasonably with changes to the threshold.
The values listed here are common starting points, and should be measured against respective 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.