This document is compatible with FastGPT v4.17.0.
What the data for this category looks like
The main entities in this category are color printing, label, carton, flexible packaging, and pharmaceutical packaging processing enterprises. Daily generated documents include pre-press files, imposition sheets, printing process sheets, ink color matching records, first-piece approval samples, barcode inspection records, die-cutting and lamination records, and more. Data sources include customer-submitted manuscripts, color management systems, printing press operation data, ink and substrate batch ledgers, spectrophotometer test results, and barcode detector output data. Data updates are triggered per order, per production batch, or each time a printing plate is replaced. Each individual document contains structured and unstructured fields such as version number, color parameters, batch information, and test results.
What constraints do these data characteristics impose on the vector model and indexing process
The data characteristics of this category impose multiple constraints on the vector model and indexing process. The data includes both structured ledgers and unstructured graphic and text content. Multi-modal vector models are required to cover fields such as text and color parameters. The high-frequency update rhythm requires support for incremental indexing to avoid excessive production resource occupation from full reconstruction. Each individual document includes identifiers such as version numbers and batch numbers. The index must support precise field association to prevent confusion between documents across batches and printing plates. Pre-press files and imposition sheets have a strong corresponding relationship. The index must retain association links to ensure that full sets of production documents can be quickly matched during cross-review.
Configuration Settings
| Configuration Item | Recommended Setting Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-paragraph content length of pre-press files and process sheets, and avoids splitting key information across document chunks |
| Overlap Length | 100–150 characters | Retains the associated context of imposition sheets and corresponding process sheets, and prevents plate number information from being split |
| Similarity Threshold | 0.75–0.85 | Filters low-match degree batch data, and avoids confusion between printing files of the same model but different batches |
| Single Search Limit | Top 20 entries | Adapts to the rapid recall requirements in production scenarios, and reduces redundant data loading |
maxToken | 4096 token | Adapts to the total content length of single-batch documents, and ensures complete transmission to the vector model |
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. |
Three Common Configuration Mistakes
- Search test returns are excessively slow, with vector model loading timeout prompts displayed in the interface. Root cause: The search speed issue was incorrectly attributed to the vector model. The actual bottleneck lies in the context splicing and generation process of the language model. Context clipping was not implemented for long documents in production scenarios.
- An error "No available embedding model" is returned when calling the knowledge base. The corresponding model has been added to the configuration file and third-party channels. Root cause: The vector model’s interface address and key were not correctly bound to FastGPT’s vector database configuration, or the model’s call permissions were not verified.
- Some data cannot be recalled after indexing hundreds of thousands of packaging documents. Root cause: The chunk length was set too small, causing key plate number information on imposition sheets to be split into different chunks. Complete associated content cannot be matched during vector recall.
How to Verify Correct Configuration
- Import a single pre-press file and its corresponding imposition sheet. Search for the plate number in the FastGPT knowledge base, and check if both associated documents can be recalled.
- Perform incremental indexing for single-batch production data. Verify that newly added die-cutting records can be indexed and retrieved within a reasonable time frame.
- Adjust the similarity threshold, and verify that recall results under different thresholds match the matching accuracy required by the business.
- Check the resource occupation status of the vector database, and confirm that the indexing process does not trigger memory or storage overflow.
The values provided here are common starting points, and should be evaluated against the reader’s own samples.