What the data for this category looks like
Construction machinery ledger data sources include equipment factory certificates, monthly maintenance reports, on-site inspection electronic ledgers, and rental settlement documents. Update frequencies are divided into three categories: real-time (running data synchronization), daily (inspection information entry), and monthly (maintenance file archiving). Documents use structured fields as the core framework, with unstructured notes and attachments attached. Fields include unique equipment number, rated load (unit: tons), cumulative operating hours (unit: hours), next maintenance date, and fault code. Some documents are accompanied by equipment nameplate photos and on-site fault photos.
Constraints on vector models and indexing from these characteristics
The coexistence of structured fields and unstructured attachments in construction machinery ledgers requires the indexing workflow to support both structured metadata association and unstructured content vectorization. Scenarios with multiple update frequencies require indexing to support both incremental update and full reindexing modes. The unique equipment number, as the core identifier, must be used as a metadata indexing field to avoid repeated association of multiple documents for the same equipment. The high proportion of attached images requires adaptation to indexing configurations for image vectorization. Significant differences in the length of different ledger documents require flexible adjustment of chunking rules to adapt to both short and long texts.
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 | Adapts to the average text length of single entries in construction machinery ledgers, avoiding vectorization deviation caused by overloaded single chunk content |
| Overlap Length | 50–80 characters | Retains key context such as equipment number and operating hours between chunks, preventing breakage of associated information across chunks |
| Similarity Threshold | 0.72–0.85 | Differentiates similarity between different inspection records for the same equipment, avoiding invalid association of ledger entries |
| Single Retrieval Limit | Top 10 entries | Matches the demand for the number of associated documents per query for ledger summary, avoiding result redundancy |
defaultToken | 4096 | Adapts to the basic context length requirement for ledger summary reports |
maxToken | 8192 | Accommodates summary content of multiple cross-cycle ledger documents, meeting long text generation requirements |
Three common configuration mistakes
- Symptom: No models appear in the text understanding model dropdown when creating a knowledge base, and already added models do not show in the list. Cause: The model was not bound to the text understanding model permission group, and only the channel addition step was completed.
- Symptom: The number of chunks displayed after uploading a single document does not match the number of chunks once the document is ready, with duplicate chunks appearing. Cause: The unique equipment number was not set as the metadata indexing key, and the system mistakenly identified duplicate fields for the same equipment as new document fragments.
- Symptom: A batch reindex task is interrupted or only some documents complete updates after being triggered. Cause:
maxTokenwas not adjusted to match the context limit for batch indexing, causing large-volume documents to trigger timeout interruptions.
How to verify correct configuration
- Navigate to the knowledge base settings page, check the vector model dropdown list, and confirm that all added target models are displayed as selectable options.
- Upload a single typical construction machinery ledger document, check the chunk preview interface, and confirm that the chunk length and overlap length match the preset configurations.
- Initiate a ledger summary query, check the number of retrieved results, and confirm that it matches the preset single retrieval limit.
- Trigger a full reindex task, wait for the task to complete, and check that there are no abnormal duplicate chunk counts for all documents.
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.