What Data Looks Like for This Category
Production ledgers for complete home appliance manufacturing include configured BOMs, assembly work instructions, refrigerant charging records, electrical safety test reports, performance sampling inspection reports, and repair records. Data sources include ERP/MES systems, component batch ledgers, tightening and charging equipment logs, safety compliance tester data, performance laboratory test results, and after-sales fault feedback data. Data updates follow three cadences: real-time updates per process for each complete unit, shift-level aggregation of team production data, and scheduled generation of sampling inspection reports. Fields include component batch numbers, tightening torque values, refrigerant charging amounts, test voltages, and corresponding physical units.
Constraints Imposed on Vector Models and Indexing
Multi-source heterogeneous data formats require vector indexes to support mixed input of structured logs, semi-structured reports, and unstructured documents. Field naming rules must be unified in advance. Frequently updated ledger data requires indexes to support incremental updates, avoiding performance losses from full index rebuilding. The full-process ledger for a single complete unit includes many process parameters with units. The vector model must accurately recognize the semantic association between parameters and units, preventing incorrect matching of values with different units. Mixed production of multiple models results in mixed ledgers. Similarity matching must accurately distinguish model fields, avoiding recall of irrelevant model production data and reducing misassembly risks.
How to Configure Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the content density of single-page documents such as assembly work instructions and safety compliance reports, avoiding semantic breaks in in-chunk process descriptions |
| Overlap Length | 100–200 characters | Retains cross-chunk process association data, such as front and rear torque connection information for tightening processes |
maxToken | 8192 token | Supports complete semantic extraction for long documents such as full BOM lists and monthly repair summary reports |
defaultToken | 1024 token | Serves as the default vector extraction length for a single ledger entry, balancing recall accuracy and computational resource overhead |
| Similarity Threshold | 0.75–0.85 | Distinguishes ledger data for the same model with different batches, preventing mixed recall caused by model confusion in mixed production lines |
| Single Search Limit | Top 10 entries | Covers the full-process production ledger for a single complete unit, avoiding redundant recall of irrelevant model production data |
Three Common Configuration Errors
- After configuring the Doubao embedding model, testing returns "404 page not found". The cause is failure to correctly fill in the interface address and version identifier of the model channel, resulting in requests failing to match the corresponding service.
- After switching the knowledge base vector model, the interface shows no progress and cannot switch back to the original model. The cause is that the incremental indexing task has not been terminated, and the background is still executing the index generation process for the old model.
- After importing mixed ledger data for multiple models, recall results include assembly records for irrelevant models. The cause is failure to configure the model field as a retrieval filter condition, causing similarity matching to not distinguish product models.
How to Verify Successful Configuration
- Upload the assembly work instructions and BOM documents for a single complete unit, check that the number of chunked text blocks matches expectations, with no excessively long or short blocks.
- Call the vector model test interface, input the text of a refrigerant charging record, and confirm that the returned vector data matches the configured values for
defaultTokenandmaxToken. - Input a search term containing multiple models, check that the recall results only include ledger data for the specified models, and similarity values fall within the configured threshold range.
- In FastGPT v4.17.0, switch the vector model, then view the indexing task status panel. Confirm that the task queue has no backlog and the indexing progress for the new model updates normally.
The values provided here are common starting points, and should be evaluated against the reader's own samples.