What this category of data looks like
Data for this category primarily comes from daily production documents and equipment systems of injection molded parts, automotive interior, home appliance, and medical plastic product manufacturers. Daily documents include injection molding process cards, raw material drying records, mold histories, first article inspection reports, patrol inspection forms, and material change and machine cleaning records. Data sources cover resin batch property sheets, injection molding machine parameters, mold temperature controller data, MES system records, and dimensional and appearance inspection results. Update frequency adjusts based on production batches, mold/material changes, and shift production cycles. Document fields include process parameters, equipment operation metrics, and quality inspection results, covering units such as pressure, temperature, and duration.
What constraints do these characteristics impose on the vector model and indexing link
The data characteristics of this category impose multiple constraints on the vector model and indexing link. First, the high-frequency, small-batch update rhythm requires indexes to support incremental synchronization, avoiding performance loss caused by full reconstruction. Second, document types cover both structured parameters and non-standard process descriptions, so vector models must adapt to both numeric fields and semantic understanding of natural language text. Third, production data is strongly bound to processes and quality inspections, so indexes must retain associations between fields to ensure retrieval results match the parameter requirements of specific production links. Fourth, production errors have serious consequences, so the semantic matching accuracy of recall results must meet the rigor requirements of process judgment.
This configuration is based on FastGPT v4.17.0.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the text length of injection molding process cards and quality inspection records, avoids including too many unrelated parameters in a single chunk |
| Overlap Length | 50–80 characters | Preserves contextual association between adjacent process steps, prevents process window parameters from being split apart |
| Similarity Threshold | 0.75–0.85 | Meets the rigor requirements of injection molding process parameters, filters low-correlation process descriptions |
| Single Search Limit | Top 6 results | Covers the number of associated documents for typical process questions, avoids redundant results |
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. |
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 mistakes
- Retrieval speed slows down after calling the knowledge base interface to generate an index. Symptoms include interface response time exceeding preset thresholds, or interface prompts indicating index synchronization task backlog. The cause is failing to configure incremental indexing for high-frequency updated production data, triggering full index reconstruction on each update, which increases processing time.
- Retrieval matching accuracy drops for previously imported knowledge bases after changing the embedding model. Symptoms include obvious differences in retrieval results for the same question compared to before the change, with an increase in the proportion of low-correlation documents. The cause is that vector spaces generated by different embedding models differ, and the original index vector data was not synchronized for update, requiring index reconstruction.
- Retrieval result ranking does not align with process judgment logic. Symptoms include directly relevant process parameter documents appearing in lower positions in returned results. The cause is failing to configure reranking rules based on the parameter priority of injection molding processes, relying only on basic similarity ranking without combining matching weights of process parameters.
How to confirm the configuration is correct
- Perform an index synchronization for a single batch of production data, check if synchronization time meets production cycle requirements, and adjust the match between chunk length and synchronization frequency.
- Input a typical process question, such as "How to adjust packing parameters for shrinkage defects", check if the retrieved result document types cover relevant data sources like process cards and quality inspection records.
- Compare retrieval matching results before and after changing the embedding model, confirm consistency of the vector space, and verify the effectiveness of index reconstruction operations.
- Check the retrieval result ranking logic, confirm that reranking rules for process parameters have been configured to align with priority requirements for production judgment.
The values provided here are common starting points and should be measured against the reader's own samples.