What this category of data looks like
Daily data for 12345 citizen hotline ticket handling includes documents such as request registration forms, dispatch records, handling responses, follow-up records, delay applications, return explanations, and knowledge base entries. Data sources primarily include the 12345 hotline platform, responsible handling units' business systems, official knowledge bases, follow-up evaluation data, and local hotline management specifications. Data updates occur via daily full or incremental synchronization. Each ticket contains core fields such as request content, responsible unit, handling status, and evaluation results. Overall text covers standardized government service request phrasing and personalized handling details.
What constraints these characteristics impose on vector models and indexes
Daily updated ticket data requires indexes to support incremental synchronization, to avoid excessive system resource usage from full reindexing. Ticket text lengths vary widely: short requests are only tens of characters, while complete handling records can reach thousands of characters. This requires adaptive chunking rules to avoid semantic fragmentation or information truncation. Data must accurately match request types and responsible units, which imposes clear requirements on the government semantic understanding capability of vector models. Recall results must also be strictly linked to the current ticket's handling scenario. Failure to do so will directly impact response accuracy and ticket circulation efficiency.
How to set configurations
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of ticket text, avoids overly fragmented chunks that lose context or overly long chunks that exceed model processing limits |
| Overlap Length | 100–200 characters | Retains semantic connections between adjacent chunks, ensures full coverage of key information for requests and handling processes |
maxToken | 8192 token | Adapts to the maximum input length requirements of mainstream open-source vector models and FastGPT v4.17.0, avoids truncation of important handling details in tickets |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Balances recall accuracy and recall rate, adapts to the semantic matching precision requirements of government service requests |
| Single Recall Limit | Top 10 entries | Controls the number of returned results, avoids introducing irrelevant historical tickets that interfere with response generation |
defaultToken | 1500 token | Sets the default output token length for vector model calls, ensures returned vector dimensions meet platform requirements |
Three common configuration mistakes
- Phenomenon: Vector model calls return a 503 status code, and the interface displays model call failure. Cause: The
defaultTokenvalue is not properly configured, exceeding the actual supported limit of the selected vector model, resulting in the request being rejected by the server. - Phenomenon: In a docker deployment environment, the knowledge base index progress stagnates for a long time with no obvious error messages. Cause: Incremental index mode is not enabled. Daily full synchronization of all ticket data occupies excessive system resources, causing the index process to block.
- Phenomenon: Recalled ticket content has very low relevance to the current request, returning a large number of irrelevant historical records. Cause: The similarity threshold is set too low, or chunk length is unreasonable, leading to inaccurate semantic segmentation that cannot accurately match request types and responsible units.
How to confirm configurations are correct
- View vector model call logs to confirm that the values of
maxTokenanddefaultTokenmatch the configured settings, with no text truncation or token limit exceeded errors. - Manually upload a typical request registration form, wait for indexing to complete, then initiate a related query. Check that the number of recalled related tickets matches the single recall limit setting.
- Manually adjust the similarity threshold, compare recall results under different thresholds, confirm that matching scores fall within the preset 0.75–0.85 range, with no abnormal deviations.
- View daily index task execution logs, confirm that incremental synchronization completes normally, with no timeout or failure records.
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 2026-09-28.