What the data for this category looks like
Production ledger data sources for furniture and wood products manufacturing include order design systems, sheet and lumber batch records, CNC cutting and drilling equipment logs, ERP/MES systems, and moisture content test reports. Daily documents cover cut lists, cutting schedules, edge banding and drilling programs, coating process documents, hardware lists, trial assembly and packaging records. Data updates occur per order, daily, or per material batch. Fields include panel dimensions, wood moisture content, hardware model numbers, order numbers, production batches, and more. Units include millimeters, cubic meters, percentage, and others.
How these characteristics impose constraints on the vector model and indexing link
This category is driven by custom orders. A single order corresponds to a large number of independent panel component documents, and data update frequency is high. The vector model and indexing link must adapt to semi-structured production ledger content and high-frequency incremental update requirements. Associated semantics of fields such as panel dimensions and batches must be fully preserved. Chunking and indexing configurations must avoid splitting cross-panel component associated information. They must also balance the computational overhead of single vectors and retrieval recall efficiency to adapt to multi-field hybrid matching business scenarios.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to semi-structured content of cut lists and cutting schedules, avoid splitting associated information between panel dimensions and batches |
| Overlap Length | 100–150 characters | Retain key semantics such as cross-chunk production batches and hardware matching relationships |
| maxToken | 8192 | Adapt to long text content such as coating processes and trial assembly records, fully retain process parameter details |
| defaultToken | 1024 | Control the computational overhead of single vectors, adapt to high-frequency updated production ledger data |
| Similarity Threshold | 0.75–0.85 | Distinguish the different precision requirements of panel dimension matching and hardware model matching |
| Single Search Limit | Top 10 entries | Balance recall efficiency and the associated integrity of production ledgers |
Three Common Mistakes
- Phenomenon: When calling the knowledge base interface to generate a production ledger index, the time consumption exceeds the preset threshold, and the number of returned search results is lower than expected. Cause: Chunking and recall limits were not adjusted for custom order multi-panel component documents, leading to accumulation of single-batch indexing tasks.
- Phenomenon: After replacing the embedding model, imported production ledger data cannot regenerate an index through the existing interface, and all files must be re-uploaded. Cause: Incremental index update configuration was not enabled, only full reconstruction is supported, and repeated import operations increase task costs.
- Phenomenon: When searching the production ledger, panel dimension matching results are ranked after hardware lists, which does not conform to the priority logic of the production link. Cause: Similarity weights were not adjusted for the core matching fields of the category. The default ranking only relies on global vector similarity, and does not adapt to the core associated requirements of the production link.
How to Confirm Proper Configuration
- Upload a typical cutting schedule document, check whether chunked text fragments retain complete panel dimensions and batch information.
- Trigger an incremental indexing task, verify whether newly added trial assembly records can be quickly retrieved.
- After adjusting the similarity threshold, compare sorting changes of search results to confirm alignment with production ledger matching priorities.
- Check vector calculation logs in FastGPT v4.17.0, confirm that
defaultTokenandmaxTokenconfigurations do not exceed the system's allowed range.
The values provided are common starting points and should be measured against the reader's own samples.