What Data for This Category Looks Like
Data for this category comes from stamping workshops of enterprises producing automotive panels, home appliance sheet metal parts, and hardware components. Daily documents include stamping work instructions, material utilization sheets, mold inspection logs, first and last piece records, dimensional inspection reports, and mold change records. Data sources include steel coil labels and quality certificates, punch and feeder data, mold management systems, MES systems, and coordinate measuring machine or inspection gauge data. Update frequency follows per coil, per batch, and per mold change. Document fields include material grade, batch identifier, processing parameter values, mold number, inspection dimension values, and more. Units include millimeters, kilograms, hours, and others.
Constraints Imposed on Knowledge Base Retrieval and Recall
This category of data has high update frequency. A single batch of processing involves multiple frequently modified documents. This requires knowledge base retrieval and recall to support fast incremental updates, to prevent ledger summary errors caused by outdated data. Multiple types of documents coexist. It is necessary to adapt chunking and retrieval logic for different document structures, to ensure accurate recall of content across all types, including work instructions and inspection records. Data has strong correlation. Material batches are strongly bound to quality certificates, and mold change records are strongly bound to mold inspection logs. This requires retrieval to accurately match associated fields, to avoid recalling irrelevant data. Additionally, stamping processing parameter adjustments and inspection results are directly linked to production quality. Insufficient retrieval precision can lead to batch deviations or equipment damage. As a result, matching precision requirements for recall are high.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Stamping scenarios have high matching precision requirements for parameters and documents. This interval filters low-correlation results and avoids recalling irrelevant mold inspection or mold change records |
limit | Top 8–12 entries | A single batch of processing for this category involves multiple core documents. 8-12 entries cover key data while controlling context length to avoid exceeding model processing limits |
searchMode | embedding | The query examples are natural-language or semantic questions; test this mode with representative data, not as a product default. |
usingReRank | Enabled | When recalling multiple types of documents, re-ranking further filters entries most relevant to the current production ledger query, improving retrieval accuracy |
datasetSearchUsingExtensionQuery | Calibrate based on actual measurements | This category of data has high correlation. Extended queries can supplement retrieval of associated fields, but need to be adjusted based on actual dataset structure to determine whether to enable |
collectionIds | Bind the dataset ID corresponding to the stamping workshop | Differentiate documents from different workshops or production lines, avoid recalling mold or material data from other production lines, and ensure precise retrieval scope |
Three Common Mistakes
- Phenomenon: Retrieved content does not match the input query, and may even answer unrelated questions. Cause: The
similaritythreshold is not set, or the threshold is set too low, resulting in recall of a large number of low-correlation documents, causing AI-generated content to deviate from the target. - Symptom: An updated image is not returned by knowledge-base search. Check the image's data row and training-task status. The FastGPT v4.17.2 release notes describe image imports being persisted after parsing and tracked through
parsed,indexing, andindexed; data awaiting indexing is not returned by retrieval. Recheck search results once the row reachesindexed. - Phenomenon: Number of retrieval results does not meet expectations, either too many or too few. Cause: The
limitparameter value is unreasonable, and is not adjusted based on the number of documents per batch processing for this category, resulting in context overflow or failure to cover core data.
How to Confirm Configuration is Complete
- Enter the FastGPT dataset management page, check if the bound
collectionIdsonly include document types for the current stamping workshop, and exclude irrelevant datasets. - Initiate a test retrieval, input "Mold inspection requirements for this mold change", verify that returned results only include content related to mold inspection logs, and that
similarityfalls within the preset interval. - Trigger a knowledge base update operation, confirm that frequently changed material batches and mold change records complete index updates within 10 minutes, adapting to the high-frequency update rhythm of this category.
- After enabling
usingReRank, compare the sorting of retrieval results before and after enabling, confirm that the ranking of highly relevant documents has been improved.
The values provided here are common starting points, and should be evaluated against the reader's own samples.