What this category of data looks like
This category of data comes from daily production documents and equipment systems from automotive panel, home appliance sheet metal, and hardware stamping workshops. Daily documents include stamping work instructions, material utilization rate sheets, mold inspection sheets, first and last piece records, dimensional inspection reports, and mold change records. Data sources cover steel coil labels and quality certificates, real-time data from stamping presses and feeders, mold management systems, MES systems, and coordinate measuring machine or fixture inspection data. Data updates are triggered per coil of material, per production batch, and each mold change. Document fields include specific production parameters such as material grade, mold clearance parameters, blank holder force values, and inspection dimensional tolerances.
What constraints do these characteristics impose on the knowledge base retrieval and recall link
The high-frequency updates, multi-source dispersion, and strong parameter binding characteristics of this category of data impose multiple constraints on knowledge base retrieval and recall. High-frequency updates require retrieval to prioritize recalling production data from the most recent mold change and most recent production batch, to avoid using expired parameters. Multi-source data must be divided into datasets by mold management, production batch, and quality inspection results, to prevent cross-category irrelevant data from mixing into retrieval results. For document structures with strong parameter binding, retrieval requires precise matching of process fields. Relying solely on semantic fuzzy matching may fail to accurately match process fields and parameters, leading to issues where recalled content deviates from actual processes.
How to configure settings
The following configurations are based on FastGPT v4.17.0, adapted to the knowledge base retrieval and recall needs of stamping and sheet metal forming scenarios.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Stamping process parameters require precise matching. A value too low will mix in irrelevant process descriptions. A value too high will miss qualified parameters from the same batch. |
limit | Top 6–10 entries | The number of stamping-related documents per production batch is limited. Too many entries will occupy context token quota. Too few will fail to cover complete process-related information. |
searchMode | embedding | Needs to combine process parameter semantics and field matching. Semantic mode is better suited for structured descriptions of process documents. |
usingReRank | Enabled | Stamping anomaly analysis requires associating multi-dimensional data. Re-ranking optimizes the relevance ranking of retrieval results. |
datasetSearchUsingExtensionQuery | Enabled | Can supplement retrieval dimensions by extending process questions, solving the problem of incomplete retrieval. |
collectionIds | Dataset IDs divided by production batch and mold number | Classifying by production batch and mold allows precise recall of production data corresponding to mold changes and batches, avoiding interference from cross-batch data. |
Three common configuration errors
- Phenomenon: The number of knowledge base retrieval results is insufficient, failing to cover all process requirements for the current stamping batch. Cause: The
collectionIdsfilter for datasets by production batch and mold number is not configured, thelimitparameter value is too low, or thedatasetSearchUsingExtensionQueryextended retrieval dimension is not enabled. - Phenomenon: The content returned by the AI is irrelevant to the current process question, and incorrectly references parameters from other molds or batches. Cause: The
similarityparameter value is too low, mixing in irrelevant process descriptions, or the retrieval scope is not restricted by dataset classification. - Phenomenon: Retrieval results include expired steel coil quality certificates or old mold change records, and do not use the latest data from the current production batch. Cause: Retrieval prioritizing the most recently updated datasets is not configured, or recall content is not filtered by update frequency.
How to confirm proper configuration
- Manually enter the process parameter keywords for the current stamping batch, and check whether the retrieval results include documents such as work instructions and material utilization rate sheets for the corresponding batch.
- Check the source datasets of the retrieval results, confirm that they only include production data for the current mold and current batch, and do not mix in other categories or expired data.
- Adjust the
similarityparameter range, verify whether the relevance of the retrieval results meets the needs of process analysis. There is no fixed value, and calibration should be done based on actual testing. - After enabling
usingReRank, check whether the re-ranked results prioritize displaying process parameters most closely related to the current anomaly problem.
The values provided are common starting points and should be measured against the reader's own samples.