What Data Looks Like for This Category
This category serves panel furniture, solid wood furniture, custom home goods, and door manufacturing enterprises. Daily generated documents include order breakdown drawings, cutting lists, edge banding and drilling programs, coating processes, hardware inventory lists, trial assembly and packaging records. Teams sourcing data for this category use order design systems, sheet and lumber batch ledgers, CNC cutting and drilling equipment, ERP/MES systems, and moisture content testing equipment. Data updates occur per order, daily, or per material batch. Some fields carry information with defined units, such as specific dimensions, batch numbers, and process parameters.
Constraints for Vector Models and Indexes
Documents in this category combine structured lists, semi-structured process descriptions, and unstructured drawing data. Vector indexes must support content across different formats. Frequently updated sheet batch and order data requires indexes to support incremental updates, avoiding extra time spent on full reindexing. The large number of board components and complex associated fields means chunk processing must prevent mixing content across orders, while ensuring accurate vector semantics for key fields such as dimensions and hardware model numbers. Tender documents must link to production data for specific orders. Indexes must support field-associated retrieval across multiple documents to improve matching accuracy.
Configuration Recommendations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Single sections of order breakdown lists and process documents for this category are typically moderate in length. This range preserves complete vector semantics and avoids oversized chunks |
| Chunk Overlap Length | 100–150 characters | Preserves contextual association between adjacent chunks, preventing critical information from being split across two chunks |
| Similarity Threshold | 0.75–0.85 | Filters low-match irrelevant documents, ensuring relevance for tender document retrieval |
| Maximum Search Results per Query | Top 20 results | This category has a large number of production data entries, limiting returned results improves retrieval efficiency |
maxToken | 8192 token | Adapts to long documents such as tender documents and order breakdown drawings, ensuring complete contextual input |
defaultToken | 256 token | Balances vector generation efficiency and semantic integrity for short text |
Three Common Configuration Errors
- Phenomenon: Index tasks remain in pending or running status for extended periods with no progress updates. Cause: A single batch imports too many order breakdown lists and process documents, and failure to split batches causes index queue blocking.
- Phenomenon: Embedding model calls prompt that the model cannot be found or loaded. Cause: The open-source adaptation package for Ali-emb3 was not correctly loaded in the FastGPT v4.17.0 model configuration, or the configuration path is incorrect.
- Phenomenon: Retrieved tender document fragments are incomplete, with critical dimensions or process parameters missing. Cause: The
maxTokenvalue was not adjusted based on the actual token length of the document, causing long documents to be truncated.
How to Verify Correct Configuration
- View the FastGPT v4.17.0 index task logs, confirm that the number of documents imported per batch does not exceed the configured single-processing limit, and there are no queue timeout errors.
- Test retrieval of tender documents containing specific hardware model numbers, confirm that the field matching degree of returned results meets business expectations, and there is no large amount of irrelevant data.
- Verify the
defaultTokenvalue, confirm that vector generation for short text has no semantic loss, and chunked content is coherent. - Check the enhanced index configuration switch, confirm that field associations between order breakdown data and quotation data have been linked to improve retrieval accuracy.
The values given are common starting points and should be measured against the reader's own samples.