What data this category includes
The data for this category primarily comes from CDN consoles, DNS resolution records, access logs, and monitoring alert systems. Update frequency is daily or whenever a configuration change occurs. Daily produced documents include domain access forms, cache rule configurations, refresh and preload records, and fault analysis reports. Fields cover domains, cache rule IDs, access peaks, alert thresholds, and more. There is no unified fixed format. Short documents like access forms are only around 100 characters. Long documents such as fault analysis reports can reach several thousand characters.
Constraints for vector models and indexing
Data sources are scattered and updates are frequent. Indexes must support incremental synchronization of multi-source data. This avoids performance losses from full re-imports. Document lengths vary widely. Vector models and chunking strategies must adapt to inputs of different lengths. This prevents over-chunking short documents or truncating long documents and losing critical information. Structured field metadata must be bound to vector embeddings. This allows precise association with CDN-specific operation and maintenance information during retrieval. Without this binding, specific cache rules or fault nodes cannot be located. Retrieval result accuracy directly affects operation and maintenance troubleshooting efficiency. Returned results must closely match the current query.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the common length of CDN cache rules and fault analysis reports, avoids overly fragmented or overly long chunks |
| Overlap Length | 100–200 characters | Retains contextual association between adjacent chunks, prevents core cache rule configurations from being split across different chunks |
| Similarity Threshold | 0.75–0.85 | Balances retrieval recall rate and accuracy, meets semantic matching requirements for CDN operation and maintenance documents |
| Single Search Limit | Top 10 entries | Controls the number of returned results, avoids excessive redundant cache rule or log data interfering with troubleshooting |
| Maximum Context | 4000 token | Adapts to retrieval needs for long documents, matches the default context limit of FastGPT v4.17.0 |
| Custom Vector Database Access | Configure according to existing database connection format | Adapts to the structured storage format of CDN access logs, supports importing existing data |
Three common configuration errors
- Phenomenon: No valid CDN-related results are returned after configuring a custom index. Cause: Core fields of cache rules and refresh records are not bound as index metadata. Only plain text descriptions are imported. This causes vector matching to fail to associate specific operation and maintenance objects.
- Phenomenon: Existing data cannot be read after connecting a custom vector database. Cause: The structured format of CDN access logs is not adapted. Importing directly as plain text causes deviations between vector embeddings and actual data.
- Phenomenon: A token limit exceeded error is returned when calling the vector model. Cause: A reasonable maximum context parameter is not set based on the average length of fault analysis reports. This causes incomplete truncation of long text, which cannot fully meet fault troubleshooting needs.
How to confirm successful configuration
- Manually import a typical CDN cache rule document, run a retrieval test, confirm that returned results include matching document content.
- Check the vector database connection logs, confirm there are no error messages for failed data imports.
- Configure a scheduled incremental synchronization task, verify that newly added daily refresh and preload records are automatically updated in the index.
- View the model call monitoring panel, confirm that token consumption for vector embeddings matches the preset maximum context setting.
The values listed here are common starting points. They should be evaluated against samples appropriate for the specific deployment.
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.