What the Data for This Category Looks Like
Business process-related data for construction machinery comes primarily from manufacturer official operation and maintenance manuals, on-site after-sales work orders, spare parts inventory lists, and equipment installation and commissioning documents. These data sources cover business scenarios across the full equipment lifecycle. Update rhythms fall into three categories: official manuals are synced when new models launch, after-sales work orders are updated based on daily new entries, and spare parts lists are updated monthly. Documents are structured hierarchically by model, part number, fault code, and operation steps. Fields include part model, serial number, working hours, consumable unit, and more. Some documents contain multilingual reference content.
Constraints on Vector Models and Indexes from These Data Characteristics
Because the data includes precise model and part number fields, vector chunking must retain key business scenario identifiers. This avoids breaking the binding between identifiers and operation steps. Diverse update rhythms across data sources require indexes to support incremental updates. Daily new after-sales work orders and monthly updated spare parts lists can be synced without full index rebuilding. Long texts in operation manuals and work orders require vector models to support long-context encoding. This prevents encoding failures caused by overly long chunks. Additionally, unit information in fields must be bound to the index as metadata. This ensures different consumable measurement standards are not confused during retrieval.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the paragraph length of single part or single fault in construction machinery documents, avoiding breaking the binding between operation steps and model identifiers |
| Overlap Length | 100–150 characters | Retains cross-chunk operation step connection information, ensuring smooth business process guidance |
| Similarity Threshold | 0.75–0.85 | Meets the accuracy requirements for matching fault codes and models, filtering irrelevant cross-model documents |
| Single Search Limit | Top 8 entries | Adapts to the short process display scenario of business process guidance, avoiding information overload that affects user judgment |
maxToken | 4096 | Adapts to vector encoding of long documents, covering complete equipment operation manual chapters |
defaultToken | 1024 | Configures the return token limit for single-round retrieval, controlling the output length of business guidance |
Three Common Configuration Errors
- No options appear in the interface index model dropdown menu, making model binding impossible. The cause is that the access path and authentication parameters of the local vector model are not configured correctly. The system cannot pull the list of available vector models, which is a common configuration exception issue in corresponding business scenarios.
- Retrieval results include operation and maintenance documents for non-target models, leading to incorrect business guidance. The cause is that the model field of the document is not bound as a metadata filter condition. Vector recall does not use precise identifiers of the business scenario for screening.
- Vector encoding fails for a single long document, and the system returns a token limit exceeded error. The cause is that the chunk length setting exceeds the
maxTokenconfiguration value. The appropriate chunk size is not split according to the document structure, leading to the encoding process exceeding system limits.
How to Verify Successful Configuration
- Upload an operation and maintenance manual for a specific excavator model. Check that the chunked text retains complete operation steps without obvious content breaks.
- Initiate a test query for "XX model excavator hydraulic oil replacement steps". Confirm that the retrieval results only include document content for the corresponding model, and the operation steps are coherent and complete.
- View the vector model operation logs. Confirm that there are no token limit exceeded errors, and the retrieval response time meets business expectations.
- Check the index model configuration interface. Confirm that the dropdown menu displays the configured local vector model options, with no blank or abnormal prompts.
The values provided 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.