Data Profile for This Category
This category covers documents tied to the full lifecycle of special equipment. Data sources include special equipment safety supervision systems, inspection and testing institution systems, user unit ledgers, and operator certificate databases. Daily documents include use registration certificates, periodic inspection reports, maintenance records, hidden danger rectification notices, accident reports, and operator certificates. Each document contains fixed fields such as equipment number, inspection cycle, maintenance unit, and operator qualification number. Data updates occur monthly. Single updates include multiple document types in one batch. Individual document lengths vary widely. Document lengths range from hundreds of characters for certificate files to thousands of characters for inspection reports.
Constraints for Vector Models and Indexes
Monthly batch data updates require indexes to support incremental updates and bulk imports. This avoids extra resource consumption from full index rebuilding. Mixed multi-type document structures require chunking strategies adapted to different text lengths. The strategy must also preserve context for cross-document linked fields such as equipment number and inspection cycle. This prevents linked information from being split. The requirement for precise matching of fixed fields means vector indexes must balance semantic similarity and field-level exact filtering. This avoids irrelevant equipment search results interfering with supervision verification work. Dispersed data sources require a unified index entry and data format conversion rules. This ensures documents exported from different systems can be properly parsed and vectorized.
Configuration Recommendations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk length | 800–1200 characters | Adapts to the common length of special equipment documents. Avoids splitting linked information such as inspection cycles and maintenance records. Also controls the calculation accuracy of single-block vectors. |
| Overlap length | 100–150 characters | Preserves key cross-chunk context such as equipment number and inspection cycle. Ensures linked information can be fully matched during retrieval. |
| Similarity threshold | 0.75–0.85 | Differentiates between inspection reports and qualification documents for similar equipment. Avoids low-match irrelevant results interfering with supervision verification work. |
defaultToken | 8192 | Adapts to the context window limits of most mainstream embedding models. Covers the content length of a complete single inspection report. |
maxToken | 16384 | Adapts to the total token limit for monthly bulk indexing. Avoids single bulk imports exceeding the processing limits of the model or database. |
| Single search limit | Top 10 entries | Matches the supervision scenario need to verify multiple linked documents. Also controls resource consumption for a single retrieval. |
Common Misconfiguration Issues
- Issue: Index creation fails when deploying a PG database via Docker, with the host configured as 8c16G without a GPU. Cause: Chunk parameters are not adjusted. The single-batch index data volume exceeds the memory threshold. This causes the container to run out of resources.
- Issue: Persistent errors occur when connecting to the OneAPI embedding model. Vector generation fails. Cause: The indexing model is not configured separately. The default global model channel is called. No dedicated API address or key for the embedding model is specified.
- Issue: A 503 error appears after starting via docker-compose. The prompt indicates no available channels for the specified embedding model. Cause: The CHATAPIKEY environment variable is not configured correctly. The container is not recreated to load new environment configurations.
How to Validate Correct Configuration
- Manually upload a single periodic inspection report. Check that the system index generation log has no prompts for chunking failures or vector generation failures.
- On the model configuration page in system settings, confirm that the indexing model and conversation model are set separately. The API address and key for the embedding model are configured independently.
- Run a monthly bulk indexing test. Check that the number of records in the corresponding vector table in the PG database matches the total number of uploaded documents.
- Submit a retrieval request for equipment qualification verification. Check that the similarity values of returned results fall within the preset threshold range.
The values provided here are common starting points. They should be evaluated against samples specific to the target environment.
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.