What data for this category looks like
The main entities in stamping and sheet metal forming scenarios are stamping workshops of enterprises producing automotive body panels, home appliance sheet metal parts, and hardware components. Daily generated documents include stamping work instructions, material utilization rate sheets, mold inspection logs, first/last piece records, dimensional inspection reports, and mold change records. Data sources include steel coil labels and quality certificates, operation data of punch presses and feeders, mold management systems, MES systems, and inspection data from coordinate measuring machines or inspection tools. Data updates synchronously per coil of material, per batch of materials, and each mold change. Documents contain fields such as part number, mold number, inspection values, and timestamps, with mixed structured and unstructured data storage characteristics.
What constraints these characteristics impose on the vector models and indexes link
Data for stamping and sheet metal forming updates frequently, with updates triggered by batches and mold change nodes. Indexes must support incremental updates and batch-grouped retrieval. Multi-source heterogeneous data formats require chunking logic adapted to different document types. For example, use differentiated chunking strategies for long texts like work instructions and structured fields in inspection reports. Mold and mold change processes have a significant impact on quality stability, so retrieval must prioritize associating historical data for the corresponding mold. Indexes must support filtering by mold number and batch number. Additionally, data fields contain a large number of engineering values and professional terms, so vector models must adapt to the semantic characteristics of industrial scenarios to avoid semantic bias from general-purpose models.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the text length of stamping work instructions and dimensional inspection reports, ensuring each chunk contains complete process or inspection information |
| Overlap Length | 100–150 characters | Connect the context of adjacent chunks to avoid truncation of cross-chunk professional terms |
| Similarity Threshold | Calibrated via actual testing | Adapt to the semantic similarity judgment standards for quality issues in stamping scenarios |
maxToken | 4096 | Match the maximum input token limit of mainstream industrial embedding models |
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. |
| Reranking | Enabled | Filter redundant results recalled from multi-source data and improve the relevance of defect analysis |
Three common mistakes
- Online recall tests do not display reranked results: Reranking configuration was not enabled during the knowledge base index setup, or the reranking model’s API configuration did not take effect.
- Embedding models cannot connect to OneAPI: API address, key, or version parameters for the embedding model were not configured correctly, causing normal forwarding of requests to fail.
- No image indexing option after local deployment: The image indexing dependency package for FastGPT v4.17.0 was not installed, or the deployment script did not enable related functions.
How to confirm the configuration is correct
- Upload a stamping work instruction, view the knowledge base chunk preview, and confirm that the chunked segments contain complete process descriptions and parameter information.
- Initiate a query containing mold numbers and quality defect keywords, and check that the number of recalled results matches the set single search limit.
- After enabling the reranking function, compare the sorting of recalled results before and after enabling to confirm that results with higher relevance appear at the top.
- View the embedding model’s call logs to confirm that the
defaultTokenandmaxTokenconfiguration parameters were correctly read and applied.
The values provided are common starting points and should be measured against the reader's own samples.