What the Data for This Category Entails
Typical entities are manufacturers of air conditioners, refrigerators, washing machines, and small home appliances. Daily documents include configuration BOMs, assembly work instructions, refrigerant charging records, electrical safety test reports, performance spot check reports, and repair records. Data sources include ERP/MES systems, parts batch ledgers, tightening and charging equipment logs, safety tester data, performance lab test results, and after-sales fault databases. Update frequency follows per single product, shift production batch, or spot check schedules. Document fields include material codes, process parameters, test values, batch numbers, and other relevant fields. Some documents include physical images and tabular process data.
Constraints on Knowledge Base Retrieval and Recall
Mixed-model, mixed-line assembly production requires retrieval results to be strictly filtered by product set to prevent incorrect assembly. Frequently updated per single product or batch data requires incremental indexing support to ensure retrieval content matches the latest production status in real time. Documents include structured tables, numerical parameters, and images, so the retrieval system must support multimodal content parsing and structured data matching. Core documents such as safety and performance test reports have high retrieval confidence requirements. Low-match results must be restricted to support production safety and compliance.
How to Configure
This configuration uses FastGPT v4.17.0’s knowledge base retrieval capabilities, adjusted for the business characteristics of complete home appliance manufacturing:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches the precision requirements for core business fields such as safety parameters and process values, and avoids mixing in low-correlation non-target model documents |
limit | Top 8 entries | Adapts to the single-retrieval demand for multiple model reference documents during mixed-line assembly, and controls context length to fit business verification scenarios |
searchMode | embedding | Use semantic/vector retrieval. Exact-field, metadata, batch, and document-type constraints require separate filters and are not enforced by this mode. |
usingReRank | Enabled | Raises the ranking priority of high-confidence safety and performance test documents to meet the strict verification standards for cross-review |
datasetSearchUsingExtensionQuery | Set based on actual calibration | Adapts extended recall for exclusive business terms such as product models and process codes, and covers professional term variants used by frontline engineers |
collectionIds | Bound to the collection ID tied to the current production batch | Strictly filters non-target model documents during mixed-line production to avoid incorrect assembly or mixed loading risks |
Three Common Misconfigurations
- Retrieval results only return text content, and do not include physical images or process diagrams within documents: This occurs because multimodal embedding configuration is not enabled, and only text content is indexed.
- Excel-format BOMs and charging record tables uploaded have low retrieval matching accuracy, and cannot accurately extract process parameters within cells: This occurs because table structured parsing function is not enabled, and content is only split as plain text.
- Retrieval results include product documents from other production batches, leading to mixed-line related incorrect matches: This occurs because the
collectionIdsparameter is not correctly configured, and the exclusive collection for the current production batch is not bound.
How to Verify Proper Configuration
- Execute a retrieval request bound to the current production collection, check the source field of returned results, and confirm that only documents within the exclusive collection of the current batch are included.
- Upload an Excel document containing structured tables, submit a retrieval for parameters within the table, and verify that accurate cell values and field names are extracted in the results.
- Upload a document with product appearance or process step images, submit a targeted retrieval, and confirm that image content can be normally recalled and associated with corresponding text descriptions.
- Adjust the
similaritythreshold and submit a retrieval, and observe whether the matching accuracy of returned results meets the precision requirements of business verification.
The values provided here are common starting points, and should be evaluated against local sample data.