What data for this category looks like
This category covers panel furniture, solid wood furniture, custom home goods, and door manufacturers. Organizations in this category generate daily documents including order breakdown drawings, cutting lists, edge banding and drilling programs, coating process documents, hardware bill of materials, assembly trial records, and packaging records. Organizations source data primarily from order design systems, sheet and wood batch ledgers, CNC cutting and drilling equipment, ERP/MES systems, and moisture content testing equipment. Update frequency aligns with individual orders, daily cycles, or individual material batch changes. Core fields include panel dimensions, material numbers, process parameters, and test results, among others.
How these characteristics impose constraints on vector models and indexing
Organizations collect data from scattered sources with inconsistent formats. Cross-system documents require format standardization before indexing can proceed. Documents include both structured lists and unstructured process drawings. Vector models must extract both text and image features to support indexing for multiple document types. Update frequency changes with orders and material batches. Indexes must support incremental synchronization to match real-time data rhythms, avoiding resource consumption from full index rebuilds. Custom orders lead to large variations in individual document length and structure. Chunking strategies must adapt to content units of different lengths to ensure complete vector extraction.
Configuration Recommendations
The following configurations are provided for this category scenario on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of documents for this category such as order breakdown drawing descriptions and cutting lists, avoids vector extraction deviation caused by overly long single chunks |
| Overlap Length | 100–150 characters | Retains contextual association between adjacent chunks, ensures that cross-chunk process parameter information is not lost during retrieval |
| Similarity Threshold | 0.75–0.85 | Accurately matches key audit parameters such as panel dimensions and hardware models, avoids interference from low-sensitivity irrelevant results |
| Single Search Limit | Top 8 entries | Covers the typical number of panels per order for this category, while controlling resource overhead during retrieval |
maxToken | 4000 token | Adapts to the vector indexing limit for long text documents such as coating process documents and assembly trial records, avoids content truncation that would lose critical information |
defaultToken | 150 token | Optimizes vector extraction efficiency for short text content such as hardware lists and batch ledgers |
Three Common Configuration Errors
- Phenomenon: A call failure occurs when connecting to Tongyi multi-modal vector models. The interface returns the error message "The model does not support the current call method". Cause: The access rules for the multi-modal model are not configured according to system requirements, so the system cannot complete verification of the model call link.
- Phenomenon: A large number of irrelevant panel size data appear in retrieval results. Matching accuracy does not meet audit requirements. Cause: The weight of the vector model is not adjusted for the core audit fields of this category. Generic parameters are used directly for retrieval.
- Phenomenon: Custom RAG retrieval only returns content from a single document. It cannot associate the size matching relationship between panels and installation sites. Cause: The linkage logic between vector indexes and knowledge graphs is not configured. Retrieval is only based on flat entities, and cross-document association relationships are not introduced.
How to Confirm the Configuration Is Correct
- Upload typical cutting lists and order breakdown drawings for this category. Confirm that the index generation status has no errors, and document content has no abnormal truncation.
- Enter audit queries targeting panel dimensions or hardware models. Check that the relevance of retrieval results matches the preset matching logic, and the number of results matches the configured single search limit.
- Adjust the configuration parameters of the vector model, regenerate the index and perform retrieval. Confirm that the relevance of results changes as expected with parameter adjustments.
- Test the drawing retrieval function after connecting the multi-modal model. Confirm that after uploading order breakdown drawings, associated process parameters and cutting data can be returned.
The values provided are common starting points and should be evaluated against local sample data.