What data for this category looks like
The main data for this category comes from daily work outputs of customer success managers, service managers, and technical support leads. It includes SLA term documents, monthly service reports, availability statistics tables, customer review minutes, and more. Data sources cover monitoring system operation data, service records from ticket systems, formal contract texts, and customer communication logs. Data updates follow a monthly or quarterly cycle, with no need for high-frequency real-time updates. Document structures include fixed fields such as service period, indicator type, and event details. Some unstructured documents are colloquial review content. Overall data volume grows cumulatively with each service cycle.
Constraints for Vector Models and Indexing
Because data updates follow a monthly or quarterly cycle, incremental indexing does not need to be performed frequently. Batch offline index construction can be used to reduce resource consumption. The data includes two formats: structured statistics tables and unstructured review minutes. The vector model and indexing must support vectorization processing for both text paragraphs and table fields. This prevents loss of key indicator associations in structured data. Fixed service period and indicator type fields require the index to support precise recall based on time dimensions and keywords. This ensures retrieval results can be directly used for SLA indicator calculation and abnormal event attribution. The low-frequency update feature also requires the index structure to prioritize retrieval efficiency and recall accuracy. This avoids index errors that could lead to compensation disputes or customer complaints.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the paragraph length of monthly service reports and SLA term documents. Prevents splitting across key information |
| Overlap Length | 100–150 characters | Retains SLA indicator context across chunks. Ensures key associations are not lost during retrieval |
maxToken | 4000 token | Matches the input limits of commercial APIs. Supports vector processing for long documents |
| Similarity Threshold | 0.75–0.85 | Filters low-relevance historical service records. Prevents incorrect attribution |
| Single Search Limit | Top 10 results | Balances retrieval efficiency and recall completeness. Fits the data volume of monthly summary reports |
| Reranking | Enabled | Optimizes matching relevance between SLA terms and service events. Improves alignment efficiency |
Three Common Configuration Errors
- Symptom: Vector retrieval first response time exceeds 8 seconds. Hybrid retrieval takes longer. Cause: Index strategy was not adjusted for low-frequency monthly/quarterly update data. High-frequency incremental indexing is still used, leading to excessive resource consumption.
- Symptom: Index construction tasks get stuck on the final group, with no clear error prompt. Cause: A reasonable
defaultTokenupper limit was not set. Large SLA term documents trigger API call timeouts during vectorization. - Symptom: Some files fail vectorization, and knowledge base construction fails. Cause: Supported file types were not confirmed in advance. Non-standard format contract scans or unstructured audio transcription files were uploaded accidentally.
How to Confirm Configurations Are Set Correctly
- Run a single vector retrieval test. Check whether returned results include key content from target SLA terms or historical service reports.
- View FastGPT v4.17.0 index construction logs. Confirm there are no timeout or format error prompts. Tasks should complete within a reasonable time frame.
- Adjust the similarity threshold. Verify whether the relevance of returned results matches business scenario requirements.
- Batch import test SLA documents and service reports. Confirm all files complete vectorization and indexing normally.
The values provided are common starting points. They should be evaluated 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.