What this category's data looks like
Typical service targets for this category are cabinet chassis, ventilation equipment, electrical cabinet and non-standard equipment manufacturers. Daily generated documents include 3D part unfolding drawings, CNC cutting programs, bending process cards, nesting diagrams, first-piece records and process flow cards. Data primarily comes from CAD/CAM software, order BOMs, sheet metal inventory systems, laser cutters, bending machines and MES systems. Update frequency aligns with each order, daily production or each process change. This category follows a multi-variety, small-batch production model. Business processes are driven by drawings. Core workflows center on 3D part unfolding, bending coefficient compensation, nesting and leftover material management.
What constraints these characteristics impose on the knowledge base retrieval and recall link
Multi-variety, small-batch production creates large document volumes in the knowledge base and frequent updates. The retrieval system must support incremental updates and precise filtering to prevent redundant recall. Drawing-driven workflows generate large amounts of specialized terminology and structured documents. The retrieval system must balance keyword matching and semantic understanding to ensure accurate recall of specialized content such as bending coefficients and nesting rules. Document formats and fields vary widely across different orders. The system must support unified indexing for multiple document types, while avoiding interference from cross-category documents. High-frequency updates require the retrieval system’s recall delay to match production tempo, and must not disrupt response speeds for real-time equipment inspection and maintenance Q&A.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Sheet metal specialized terminology matching requires a high similarity threshold to avoid recalling irrelevant cutting and bending documents, balancing precision and recall coverage |
limit | Top 8 entries | In multi-variety small-batch scenarios, too many single-recall results increase context processing load, while too few fail to cover all content related to full-process inspection and maintenance |
searchMode | mixedRecall | Balances keyword matching for content such as CNC program numbers and bending coefficients, and semantic similarity recall for content such as unfolding logic and nesting rules, adapting to retrieval needs for specialized documents |
usingReRank | Enabled | Performs priority reordering of initially recalled results, ensuring key content such as core inspection steps and troubleshooting methods appears first, improving Q&A efficiency |
datasetSearchUsingExtensionQuery | Enabled | Supplements extended queries for specialized terms such as unfolding coefficients and leftover material management, improving recall precision for niche domain documents |
collectionIds | Bind dedicated sheet metal cutting and bending knowledge base directory | Isolates documents from other categories, avoids mixing irrelevant content in retrieval scope, ensuring Q&A only calls specialized knowledge bases for this category |
Three Common Mistakes
- Symptom: The knowledge base page displays all uploaded documents, but the model prompts the knowledge base is empty during conversation. Cause: The
collectionIdsparameter is not configured correctly, so the retrieval scope does not point to the target dedicated directory, or the vector database association relationship is not synchronized during database migration. - Symptom: Retrieval results mix a large number of stamping process-related documents. Cause: The dedicated directory is not bound, or the
collectionIdsparameter is configured incorrectly or left blank, leading to recall of cross-category documents. - Symptom: Retrieved document chunks exceed 3000, causing context overflow or response timeout. Cause: A reasonable
limitparameter is not set, orusingReRankis not enabled to streamline and sort recalled results.
How to Confirm the Configuration Is Correct
- Enter the FastGPT v4.17.0 knowledge base management interface, check if the target directory's ID matches the
collectionIdsparameter in the configuration. - Launch a test query containing specialized terminology, such as "bending coefficient compensation method", to verify that recalled results include documents related to bending process cards.
- View the retrieval log, confirm that the number of returned results matches the
limitparameter configuration, and that similarity scores fall within the 0.75–0.85 range. - Upload a new nesting diagram document, trigger an incremental update, then launch a relevant query to verify that the new document is correctly recalled.
The values provided are common starting points and should be measured against the reader's own samples.