What data for this category includes
This category of data comes from database monitoring systems, operation audit logs, SQL execution plan files, and permission application and change ticket systems. Daily generated documents include database change orders, slow query performance reports, backup and recovery records, and permission approval forms. Data updates run on a daily schedule, or trigger when a single database change occurs. Common document fields include instance identifiers, execution duration, permission scope, change content, and other related items. Some log data includes timestamp and operator information.
Constraints on vector models and indexing
This category’s data characteristics create multiple constraints for the vector model and indexing workflow. Data sources are scattered and formats vary. Differentiated chunking logic must support structured logs, semi-structured tickets, and plain-text reports. Update frequencies fluctuate sharply. When batch synchronizing changed data, avoid duplicate indexing and redundant calculations. Data with strong timeliness, such as slow query reports, requires vector index update delays to match business change rhythms. Permission-related documents have sensitive, fixed fields. Targeted chunk length control prevents truncation of critical permission descriptions. Single data volume varies widely, from tickets of a few KB to execution plans of tens of MB. Different chunking and indexing strategies must be supported.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the text length of database change orders and slow SQL reports, avoiding truncation of critical change content and execution plan details |
| Overlap Length | 50–100 characters | Retains contextual connections across chunks, adapting to the complete semantics of SQL statements spanning chunks |
| Similarity Threshold | 0.75–0.85 | Filters low-match redundant results, avoiding false recall of sensitive documents such as permission application tickets |
defaultToken | 1500 token | Adapts to the context length of single-round queries, accommodating associated information from multiple slow SQL reports |
maxToken | 4000 token | Limits the maximum length of a single index, preventing indexing failures caused by ultra-long execution plan files |
| Single Search Limit | Top 8 entries | Balances query performance and recall coverage, adapting to the precise positioning needs of operation and maintenance scenarios |
Three common misconfigurations
- Phenomenon: Batch vector model training cannot be triggered, and only single-file upload is supported for adjusting parameters and re-indexing. Cause: The configuration switch for batch indexing is not enabled, and only the single-file upload vectorization process is activated.
- Phenomenon: The number of chunks displayed in the knowledge base does not match the expected segmented count of uploaded documents, with duplicate index entries appearing. Cause: The automatic trigger switch for incremental indexing is not disabled, leading to repeated chunk generation when documents are updated.
- Phenomenon: After upgrading FastGPT v4.17.0, previously queryable database operation and maintenance documents cannot be recalled. Cause: A full vector index rebuild was not triggered after the upgrade; the vector encoding format of the old version is incompatible with the new version.
How to confirm configurations are set correctly
- Upload a typical slow SQL report, check the chunk preview interface, confirm that chunk lengths fall within the preset range, with no critical statements truncated.
- Initiate a keyword query for a permission approval ticket, verify the similarity distribution of returned results, confirming alignment with the business's tolerance for false recall.
- Trigger a full index rebuild, wait for the task to complete, compare the number of documents before and after indexing, confirming no duplicate entries are generated.
- Adjust the single search limit parameter, initiate multiple rounds of queries, confirm that the number of returned results matches the preset limit.
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 2026-10-19.