What the data for this category looks like
This category's data primarily comes from the 12345 citizen hotline platform, contractor business systems, official knowledge bases, follow-up evaluation data, and local hotline management regulations. Daily processed documents include request registration forms, dispatch records, handling responses, follow-up records, extension applications, return explanations, and standardized knowledge base entries. The data update frequency is daily. Each entry contains fields such as request number, contracting entity, request content, handling status, follow-up result, and others. The structure is relatively fixed, but the length of individual entries varies significantly.
Constraints these characteristics impose on vector models and indexes
The data for this category has scattered sources, a fixed update frequency, and large variations in individual entry length. These characteristics impose clear constraints on the vector models and indexes link. Multi-source data must first undergo format unification before being connected to the index to avoid vector generation bias. The daily update feature requires support for incremental index updates to reduce resource consumption from full reconstruction. The large variation in individual entry length requires flexible chunking rules to avoid overly short or long text impacting vector quality. The closed-loop business logic of tickets requires the index to prioritize associating dimensions of request type, contracting entity, and response content to improve recall accuracy.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | This category has large variations in individual ticket content length; this range balances text completeness and vector generation efficiency |
| Overlap Length | 100–150 characters | Prevents critical contextual breaks after chunking, ensuring complete associated information for requests and responses |
| Similarity Threshold | 0.72–0.85, calibrated via actual testing | Must distinguish similar request types and contracting entities to avoid false or missed recalls |
| Single Search Limit | Top 10 entries | Candidate results for 12345 citizen hotline tickets do not need to be excessive; controlling return volume improves response speed |
maxToken | 8192 | Adapts to the context window of mainstream embedding models, covering vector processing needs for all associated text of a ticket |
defaultToken | 2048 | Adapts to the default context requirements of basic embedding models, handling short-text scenarios |
Three common misconfigurations
- A large number of invalid short text fragments appear after chunking. This occurs because the chunking range is not adjusted based on ticket content length, and overly short follow-up records or extension applications are chunked individually.
- Vector index query timeouts occur. This happens when the single search limit is set too high, and the total token count of returned results is not restricted, leading to excessive model processing load.
- Index storage space exceeds expected levels. This stems from not setting a reasonable chunk length, which causes a single ticket to generate too many vector fragments and increases index volume.
How to confirm proper configuration
- Review the index construction logs in FastGPT v4.17.0 to confirm that daily incremental update tasks trigger normally, with no format conversion errors.
- Randomly select 3 ticket requests of different types, perform vector recall, and check whether the returned results include matching contracting entities or response content.
- Adjust the chunk length parameter to verify that the generated vector fragment length matches the preset range.
- Check the embedding model call logs to confirm that the
maxTokenanddefaultTokenconfigurations match the model's window limits.
The values given 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.