What this category's data looks like
This category of data comes from special equipment safety supervision systems, inspection and testing institution systems, user ledgers, and operator certificate databases. It covers daily documents including use registration certificates, periodic inspection reports, maintenance records, hidden danger rectification notices, accident reports, and operator certificates. Data is updated monthly. Core fields include unique device numbers, inspection cycle nodes, maintenance execution dates, hidden danger rectification statuses, operator qualification numbers, and similar items. No fictional statistical values are included.
Constraints on vector models and indexes
The monthly update cycle requires index configurations to support batch full updates, reducing operational costs from frequent incremental updates. Heterogeneous document types from multiple sources require chunking rules that adapt to content ranging from short number fields to long text reports, avoiding splitting critical business information. Configure separate index priority settings for core business fields such as unique device numbers and inspection cycle nodes, to ensure high-value content is matched first during retrieval. Align the monthly batch processing data scale with the load capacity of the index service, preventing task timeouts and interruptions to ensure response efficiency for service inquiries.
How to configure settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Covers core paragraphs of long documents such as device periodic inspection reports and accident reports, avoiding splitting critical business information such as cross-page inspection cycles and hidden danger rectifications |
| Overlap Length | 100–150 characters | Connects context between adjacent chunks, ensuring business logic across paragraphs such as inspection cycles and hidden danger rectification statuses can be fully retrieved, avoiding information gaps during service inquiries |
maxToken | 8192 token | Adapts to the input upper limit of mainstream vector models, meets vector generation requirements for long-text accident reports, and avoids truncating core business content |
| Similarity Threshold | 0.72–0.80 | Distinguishes retrieval results for similar device numbers and inspection cycles, reducing the probability of mismatching similar device models or expired inspection information during service inquiries |
| Single Search Limit | Top 10 entries | Covers multiple associated devices or related documents that need verification in supervision business, avoiding omission of critical rectification or maintenance information |
defaultToken | 1000 | Balances retrieval context length and model response speed, adapting to the fast response requirement for short queries in service inquiry scenarios |
Three common misconfigurations
- Symptom: In a Docker deployment environment, knowledge base index tasks remain in a running state with no progress updates. Cause: No reasonable batch processing threshold configured based on the monthly update data scale, leading to excessive memory usage by the index service and task blocking.
- Symptom: Calling the embedding model returns a 503 status code, prompting that no available models exist under the current default group. Cause: No dedicated group specified in the vector model configuration, or model instances within the group failed to start normally.
- Symptom: Retrieval results have low matching degree with the business requirements of service inquiries, and core fields are not recalled first. Cause: No separate index weight configured for core business fields such as device numbers and inspection cycles, leading to vector retrieval prioritizing non-critical text.
How to verify correct configuration
- View the vector model configuration page, confirm that
maxTokenis set to a value adapted to business documents, anddefaultTokenmeets the processing requirements for short queries. - Perform a small-batch test index, check whether the chunked document fragments contain complete core business fields such as device numbers and inspection cycles.
- Initiate a test query, verify whether the number of retrieval results matches the configured single search limit.
- View task logs, confirm there are no 503 status code errors from model calls, verifying normal model connectivity.
The values given here 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 2026-09-28.