What this type of data looks like
Follow-up record summaries come from the ticket closing processes of state-owned enterprises, government, and public service hotlines. They are generated via automatic call transcription or manual organization. Updates trigger in real time when tickets are completed. Daily batch data can reach tens of thousands of entries. Each single document has five core fields: unique ticket identifier, follow-up time, service recipient information, core communication content summary, and customer request classification. Some records include auxiliary fields such as follow-up duration (unit: seconds) and satisfaction score. Single entry length mostly ranges from 300 to 1200 characters.
Constraints on embedding models and indexes
Real-time update requirements mean vector indexes must support incremental synchronization. This avoids resource consumption and delays caused by full reconstruction. Multi-field structures require clear specification of embedded core fields. This prevents irrelevant metadata from interfering with semantic vector generation. Wide variation in single entry length requires adaptation to text chunking strategies for different lengths. Binding to ticket systems requires vector indexes to associate ticket metadata. This ensures retrieval results can be directly linked to the corresponding ticket workflow.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to common lengths of follow-up record summaries, avoids semantic breaks in single chunks |
| Overlap Length | 100–150 characters | Covers semantic connections across chunk boundaries, prevents key information from being split between chunks |
| Similarity Threshold | 0.7–0.85 | Meets semantic matching accuracy requirements for hotline tickets, filters low-correlation recall results |
maxToken | 4096 | Adapts to token limits of mainstream open-source embedding models, matches total character conversion needs of follow-up record summaries |
defaultToken | Use model default | Adapts to scenarios where token length is not explicitly specified, ensures basic embedding functionality operates correctly |
Three common mistakes
- Retrieval results are empty after index import. This occurs when embedded fields for follow-up record summaries are not specified, and only raw ticket metadata without generated vectors is imported.
- When deploying FastGPT v4.17.0 locally, model connectivity is normal but vector generation fails. This occurs when
maxTokenis set beyond the support limit of the locally deployed model, or when the actual character length of follow-up record summaries is not adapted to. - Incremental index update delay is too high. This occurs when incremental index mode is not enabled, and full reconstruction strategy is still used to process real-time generated follow-up record summary data.
How to confirm configurations are correctly set
- Upload a single follow-up record summary to the knowledge base. Check that the embedding task log has no errors, and generated vector data can be viewed in the index panel.
- Enter core request keywords from follow-up records. Retrieval results should include matching ticket IDs and corresponding summary content, and the number of recalled entries matches the single recall upper limit setting.
- Check index update monitoring. Confirm that follow-up record summaries generated each hour can complete incremental index synchronization within 10 minutes.
- Randomly select 10 follow-up record summaries. After converting to token counts, all do not exceed the
maxTokenconfiguration value, and no content truncation occurs.
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.