What the data for this category looks like
Data for stamping and sheet metal forming business comes from steel coil labels and quality certificates, press and feeder operation data, mold management systems, MES systems, and coordinate measuring machine or fixture inspection reports. Updates are triggered per coil of material, per production batch, and after each mold change. Daily generated documents include stamping work instructions, material utilization rate sheets, mold inspection sheets, first and last piece records, dimensional inspection reports, and mold change records. Fields include material grade, material thickness, mold number, inspection dimensions, mold change time, and more. Units include millimeters, tons, hours, and more.
What constraints these characteristics impose on the knowledge base retrieval and recall link
This category’s data has high update frequency and scattered sources. Document formats and fields vary across different systems. This requires knowledge base retrieval and recall to support incremental synchronization and multi-format adaptation. Bid documents and quotation preparation require precise matching of process parameters and material data. Retrieval results must cover core business fields, so retrieval logic must avoid interference from irrelevant data. Additionally, mold and material parameters differ across production lines. Retrieval must be limited to the dataset scope for the corresponding production line, to avoid cross-production line incorrect matches that affect bidding accuracy.
How to configure the settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Stamping parameters have high precision requirements. Low-match irrelevant documents must be filtered to ensure business relevance of retrieval results |
limit | Top 8–12 results | Bid documents need to cover multi-dimensional process parameters. Too many results will exceed the context window limit. Too few results will fail to cover complete business scenarios |
usingReRank | Enabled | Matching ranking for multi-source data must be optimized to avoid non-core parameters interfering with core retrieval results |
datasetSearchUsingExtensionQuery | Enabled | Queries for bid quotations often include composite conditions. Optimization expands the retrieval scope to match more accurate business documents |
collectionIds | Bind dedicated dataset ID for stamping workshop | Distinguish mold and material data across production lines, avoid cross-production line incorrect matches, and ensure retrieval results only target the production line for the current bid project |
Three common mistakes
- Phenomenon: Newly added first and last piece records in the knowledge base cannot be retrieved. Cause: New documents were not synchronized to the bound dedicated dataset collection, or regeneration of embedding vectors was not triggered.
- Phenomenon: The system prompts abnormal high disk usage of the knowledge base. Cause: Redundant split file blocks and expired embedding vectors were not cleaned up regularly. Storage includes original files, split blocks, and vectors without incremental optimization.
- Phenomenon: Mold parameters from other production lines appear in search results. Cause:
collectionIdswas not specified to bind the dedicated dataset, leading to recall of cross-production line data that interferes with bidding quotation accuracy.
How to confirm the configuration is correct
- Log in to the FastGPT v4.17.0 knowledge base management interface, view the bound
collectionIds, and confirm the dedicated document collection for the corresponding stamping workshop. - Submit a test query containing a specific material grade and material thickness, and check whether the matching degree of retrieval results falls within the preset
similarityrange. - Add a simulated mold change record document, wait for index generation, then execute the same query, and confirm that the new content can be retrieved normally.
- View the system disk usage statistics, confirm that the storage proportion of split file blocks and embedding vectors meets expectations, and there is no accumulation of redundant data.
The values given are common starting points and should be measured against the reader's own samples.