What Data Looks Like for This Category
Data for this category originates from multiple business documents of automotive component suppliers covering braking, steering, chassis, and other systems. Documents include control plans, process flowcharts, work instructions, first-piece and patrol inspection records, SPC reports, PPAP materials, and 8D reports. Supported vectorized file types include text, tables, and structured parameter files. Data sources include customer drawings and special characteristic lists, ERP/MES systems, measurement and inspection tool data, equipment process parameters, and supplier quality certificates. Data is collected continuously per batch and shift. Documents contain fields such as process parameters, test values, and anomaly records. Some documents must comply with customer-specific unit and format requirements.
Constraints Imposed by These Characteristics on Vector Models and Indexing Workflows
The multi-source heterogeneous data structure, high-frequency update cadence, and strict compliance requirements of this category impose multiple constraints on vector model and indexing workflows. Cross-format business documents (drawing text, equipment parameters, report data) require models to support multiple types of text encoding, and adapt to semantic extraction for different fields. The continuous collection cadence per batch and shift requires indexes to support incremental synchronization, to avoid resource usage from full reindexing. Customer-specific special characteristic fields and unit requirements require retaining metadata associations during indexing, to ensure retrieval results match business rules. Large-scale data from mass production requires limiting the size of single-shard indexes to control retrieval latency.
How to Set Configurations
The following configurations are designed based on the FastGPT v4.17.0 interface and logic:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of process documents and patrol inspection records for this category, preserves semantic integrity of special characteristics |
| Overlap Length | 100–150 characters | Connects semantics of adjacent chunks, ensures consistent retrieval of special characteristics across chunks |
| Similarity Threshold | 0.72–0.85 | Matches semantic matching accuracy for anomaly records and PPAP materials in this category, filters low-relevance historical data |
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 | 8192 | Supports long-text encoding for complete 8D reports and SPC reports |
| Single Search Limit | Top 20 entries | Adapts to the reference range of historical anomaly batches for root cause analysis, avoids redundant results |
Three Common Mistakes
- Symptom: All vector retrieval scores are identical when running a Docker image. Cause: The vector model configuration cache file was not properly mounted during image packaging, leading to abnormal model loading. All text is encoded to identical vectors.
- Symptom: Hybrid retrieval takes longer than 10 seconds, with no response for single retrieval. Cause: Incremental indexing was not configured, leading to full scanning of batch historical data for this category. This causes unexpected increases in index scanning latency.
- Symptom: Index construction stops at the final dataset and cannot complete. Cause: No reasonable
maxTokenvalue was set. Long texts such as 8D reports and PPAP materials cannot be encoded, blocking index construction.
How to Confirm Configurations Are Set Correctly
- Upload a single 8D report and SPC report. Check that chunked text fragments retain complete special characteristic fields, with no semantic breaks.
- Submit a batch retrieval request. Observe whether retrieval latency meets response requirements for production workflows. Adjust configuration items to fit actual scenarios.
- Compare retrieval results between local environments and Docker containers. Confirm that no abnormal identical vector scores appear.
- Verify retrieval permission associations for sensitive documents. Ensure only authorized users can access corresponding vector results.
The values provided are common starting points and should be measured against the reader's own samples.