Reference only; verify against the target FastGPT release and deployment documentation.
What the Data for This Category Looks Like
Data primarily comes from maintenance inspection logs, equipment ledgers, employee office documents, attendance records, and ticketing systems. Update frequency varies significantly by data type. Inspection logs update daily or weekly. Equipment ledgers sync when hardware changes. Office documents are revised on demand. Each individual document includes dedicated fields such as tower number, inspection point, and fault level, with attached units like number, meter, and volt. Some long documents contain multiple pages of inspection steps and rectification plans.
Constraints Imposed on Vector Models and Indexing
This category’s data includes structured device fields and unstructured inspection documents. It also features real-time updated inspection logs and low-frequency updated equipment ledgers. Single documents may contain multi-page professional content. Mixed data types require indexes to support both structured field encoding and vector generation for unstructured text. High-frequency updated log data requires indexes to support incremental synchronization, avoiding resource consumption from full reconstruction. Long professional documents require retaining point and device association information during chunking to prevent semantic fragmentation. Properly set defaultToken and maxToken to adapt to documents of different lengths, balancing semantic completeness and resource consumption.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to semantic completeness of professional long texts such as inspection reports and equipment ledgers, avoids content fragmentation in single chunks |
| Overlap Length | 100–150 characters | Retains contextual association between chunks, prevents semantic loss of cross-chunk professional terms |
defaultToken | 1000 token | Matches the average length of internal office documents, controls resource consumption for single-round vector generation |
maxToken | 4000 token | Covers complete work orders and inspection process documents retrieved in a single call, meets context invocation requirements |
| Similarity Threshold | 0.75–0.85 | Accurately matches semantic features of equipment faults and employee requests, reduces recall rate of irrelevant content |
| Single Search Limit | Top 20 entries | Balances recall comprehensiveness and interface response speed for internal office scenarios |
Three Common Misconfigurations
- Phenomenon: After calling the file upload API, the interface returns success, but index generation progress cannot be confirmed via the interface or logs. In some scenarios, an error occurs where search is triggered before index completion. Cause: No asynchronous task status monitoring logic is configured, and only the synchronous return of the upload interface is used to judge task completion status.
- Phenomenon: Professional terms are fragmented in vector recall results, such as "tower zinc coating thickness inspection" being split into two unrelated semantic fragments. Cause: Chunk length is set below 800 characters, failing to cover complete professional semantic units.
- Phenomenon: Search results have very low matching degree with employee requests, with a large number of irrelevant inspection logs returned. Cause: Similarity threshold is set higher than 0.85, filtering out valid content with similar semantics but minor detail differences.
How to Confirm Configurations Are Correctly Set
- Upload a single complete inspection report document, query progress via the asynchronous task interface provided by the system, confirm that the index generation completion status feedback works normally.
- Manually adjust chunk length and overlap length parameters, import test documents containing professional terms, verify that recall results do not show semantic fragmentation.
- Initiate a simulated search for equipment fault descriptions or daily employee requests, check that recall result matching logic conforms to preset rules.
- View vector index storage statistics, confirm that data usage complies with server resource configuration requirements.
The values provided here are common starting points and 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 2026-09-28.