This document applies to FastGPT v4.17.0.
What the Data for This Category Looks Like
Typical entities for this category are suppliers of automotive braking, steering, chassis, interior and exterior trim, and powertrain systems. Daily generated documents include control plans, process flowcharts, work instructions, first-piece and patrol inspection records, SPC reports, PPAP materials, and 8D reports. 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 update frequency is synchronized with each production batch and each shift output; some equipment parameters and patrol inspection records are collected continuously. Fields include special characteristic numbers, process parameter thresholds, non-conforming product rates, root cause analysis conclusions, and some documents have unit annotations such as torque values and pressure parameters.
What Constraints Do These Characteristics Impose on Vector Models and Indexing
Multi-source heterogeneous data formats vary significantly. Structured SPC reports and patrol inspection records exist alongside non-standardized process documents and reports. This requires indexing to support mixed-format parsing and vectorization. High-frequency batch update data creates continuous indexing refresh pressure, so indexing must adapt to incremental indexing modes to match production rhythms. Special characteristics of automotive components have strong associations, so vector retrieval must accurately match process parameters and customer requirements, avoiding generalized matching of irrelevant content. Some documents include compliance annotations, so indexing must retain field-level associations to prevent loss of key compliance information during retrieval. Continuously collected real-time equipment data requires indexing update delays to be controlled within a reasonable range to support rapid response needs at production sites.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk length | 800–1200 characters | Adapts to the typical paragraph length of single process documents in this category, avoiding cutting key process parameters and special characteristic associated content |
| Overlap length | 100–150 characters | Retains cross-chunk process parameter context, preventing loss of continuous process requirements during retrieval |
| Similarity threshold | Calibrated based on actual measurements | Must match the accuracy requirements for special characteristic matching in this category, avoiding mismatching of unrelated process documents |
| Single search limit | Top 8 entries | Balances retrieval efficiency and recall coverage, adapting to multi-document association needs during batch audits |
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 Misconfigurations
- Symptom: After uploading an Excel-format SPC report, vectorization is completed but the index shows as empty. Cause: Structured data parsing rules are not configured, causing numerical and text fields in the report to not be correctly associated, preventing the generation of valid vectors.
- Symptom: The knowledge base remains in the "Indexing" state for more than 600 seconds after upload. Cause: Incremental indexing mode is not enabled, and full indexing takes too long to process high-frequency updated batch production data, exceeding the system's default timeout limit.
- Symptom: Unrelated PPAP materials appear in retrieval results, and similarity matching does not meet expectations. Cause: The similarity threshold is set too low, causing documents with low relevance to be recalled, making it impossible to accurately match the current audit's process parameters.
How to Confirm Proper Configuration
When uploading a typical work instruction document, review the vector generation log and confirm that maxToken does not trigger truncation prompts. When submitting an SPC report containing special characteristics, verify that retrieval results include associated control plan documents and confirm that field-level associations function correctly. When simulating batch uploads of multiple 8D reports, check the indexing progress and confirm that update delays under the incremental indexing mode match production rhythm requirements. When adjusting the similarity threshold and conducting retrieval tests, verify that the relevance of recall results matches the accuracy requirements of the current audit.
The values provided are common starting points and should be measured against the reader's own samples.