What Data for This Category Looks Like
Data sources for this category include resin batches and property sheets, injection molding machine operating parameters, mold temperature controller data, production MES systems, and dimensional and appearance inspection results. Daily documents cover injection molding process cards, raw material drying records, mold histories, first article inspection forms, team patrol inspection forms, and material change and machine cleaning records. Data updates are triggered alongside production batches, mold and material change operations, and daily production cycles. Each individual data entry includes fields such as process parameter values, equipment operating hours, and inspection results, with some fields carrying units such as ℃, MPa, and hours.
What Constraints These Characteristics Impose on Vector Models and Indexes
Data for this category is updated frequently alongside production batches, mold and material changes, and daily production cycles. This requires indexes to support incremental refresh to avoid excessive computing resource usage from full reconstruction. Daily documents include multiple types of structured and semi-structured content, with fields carrying different units and business meanings. When splitting vectors into chunks, field association information must be retained to avoid losing business context after splitting. Single data entry parameter accuracy requirements are high. Vector models must adapt to the encoding logic of numeric fields, while indexes must support filtering recall results by dimensions such as production batch and mold number to ensure accurate context matching during audits.
How to Set Configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the length of single-segment business information in documents such as injection molding process cards and patrol inspection forms, avoiding loss of process parameter associations after splitting |
| Overlap Length | 50–80 characters | Retains parameter context of adjacent chunks, preventing key information in material change and machine cleaning records from being split |
| Similarity Threshold | 0.75–0.85 | Matches the similarity requirements of injection molding process parameters and inspection results, filtering low-correlation historical data |
| Vector Database Connection | Connect to self-hosted Qdrant instance | Adapts to custom vector database requirements without migrating existing production environment stock data |
maxToken | 4096 token | Adapts to the input length limits of large models such as Tencent Hunyuan, ensuring complete transmission of context from process cards and inspection records |
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 Misconfigurations
- Recall results include injection molding process parameters from different mold numbers. Cause: No index filtering rules configured by mold number or production batch, resulting in recall scope covering unrelated production batch data.
- Vector search requests time out. Cause: Chunk length exceeds 1500 characters, and the single search limit is set to 15 entries, exceeding the basic processing threshold of FastGPT v4.17.0.
- Matching results include property data from different resin batches, leading to misjudgment of material mixing risks. Cause: No index filtering configured by resin batch dimension, resulting in incorrect matching of cross-batch data.
How to Verify Proper Configuration
- Upload a single injection molding process card document to trigger vector index generation, and check whether the split text fragments retain complete process parameters and unit information.
- Initiate an audit query for a specific mold number, confirming that the recall results only include historical production data for that mold, with no unrelated cross-mold content.
- Test the
maxTokenconfiguration by inputting a complete injection molding process card and inspection record, confirming that the returned context is not truncated. - Adjust the similarity threshold and initiate a repeated query, observing whether the number of recall results changes as expected.
The values provided are common starting points and should be measured against the reader's own samples.