What the data for this category looks like
The typical entities in this category are enterprises engaged in color printing, labels, cartons, and similar production. Daily data includes pre-press files, imposition sheets, printing process sheets, ink color matching records, first-piece sample approvals, barcode test results, and more. Data sources cover customer manuscripts, color management systems, printing press operation data, ink and substrate batch records, spectrometer test results, and other sources. Data is updated to generate new content for each order, each batch, or each plate change. Individual documents contain fields such as text and images, plate number information, color parameters, batch numbers, and others. There is no unified fixed unit, and values are dynamically adjusted based on test items and process requirements.
What constraints these characteristics impose on the vector models and indexes link
The data for this category has a high update frequency, and single-batch data is linked to multiple process records. This requires indexes to support high-frequency incremental updates, avoiding excessively long full index construction times. Mixed multi-type documents (text, image-related parameters) require indexes to support cross-source data associated retrieval, covering full-link information from pre-press, printing, and post-processing procedures. Data fields for individual pieces are scattered across different systems, so indexes must be used to establish batch-level associated matching. Plate-making errors can be replicated quickly, requiring retrieval response speeds to match production cycles and avoid delaying batch release. At the same time, the volume of traceability data associated with batches is large, so storage usage for single-batch indexes must be limited to avoid resource overload.
How to set the configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the semantic integrity of text such as pre-press files and process sheets, avoiding breaks in in-block process information |
| Overlap Length | 100–150 characters | Cover cross-block associated process details such as imposition sheets and batch records, preventing key information from being split |
| Similarity Threshold | 0.75–0.85 | Distinguish subtle differences in printing process parameters, adapting to the judgment logic for defective batches |
| Single Search Limit | Top 8 results | Match the rapid response requirements of production links, avoiding excessive redundant results interfering with defect analysis |
defaultToken | Set from representative sample documents | defaultToken is the default token count for direct text chunking; tune it on representative source documents. See the v4.17.2 model-field reference. It is not a vector dimension, embedding-model input cap, or retrieval/LLM context. |
maxToken | Set to the selected embedding model/service maximum supported input tokens | maxToken is the input-token maximum supported by the selected embedding model/service; verify it in that provider configuration. See the v4.17.2 model-field reference. It is not retrieval/LLM context or a FastGPT-wide fixed cap. |
Three common mistakes
- Phenomenon: Search results return historical process records unrelated to the current printing batch. Cause: Index filtering rules are not set according to batch numbers, resulting in cross-batch data being included in the search scope.
- Phenomenon: The index status always shows not ready, and retrieval cannot be started. Cause: Incremental update trigger logic is not configured, and full index construction cannot be completed under high-frequency updated batch data.
- Phenomenon: Retrieval results do not include associated information of plate-making text and images. Cause: Chunk length is set too small, splitting image descriptions and plate number information in pre-press files into independent blocks, losing associated semantics.
How to confirm the configuration is correct
- Upload a single pre-press file and the corresponding batch process sheet, verify whether the retrieval results include associated information such as plate numbers and ink parameters.
- Use the incremental index function of FastGPT v4.17.0 to simulate high-frequency incremental upload of batch data after plate changes, confirm that the index can complete updates and become ready within 600 seconds.
- Enter search terms containing descriptions of typos and registration deviations, verify whether the similarity threshold adapts to the defect judgment logic for this category.
- Check the configurations of
defaultTokenandmaxToken, confirm that complete single-batch process records are not truncated.
The values provided here are common starting points, and should be evaluated against the reader's own samples.