What this category of data looks like
This category of data is generated by manufacturing enterprise change management teams. It covers ECN/ECR, change assessment reports, drawings and BOM version files, process validation reports, production switching plans, inventory disposal documents, and customer approval documents. Data sources include PLM, ERP, MES systems, supplier materials, validation test records, and customer special requirements. Update frequency is irregular, with updates triggered each time a process or engineering change occurs. Most documents contain fields such as version identifiers, material codes, process parameters, and approval nodes. There is no unified fixed format, but core fields revolve around manufacturing elements associated with changes.
What constraints do these characteristics impose on the vector model and index link
Data sources are scattered and formats are inconsistent. Vector models must support vectorization processing of multi-source heterogeneous text to avoid losing change-related information due to format differences. Irregular change updates require indexes to support incremental synchronization, avoiding performance losses caused by full reconstruction. Documents contain core fields such as version identifiers and material codes. Indexes must prioritize vector matching of these fields to ensure retrieval results are closely tied to manufacturing elements associated with changes. A single change is associated with a large number of documents. The index recall limit must adapt to the retrieval needs of batch documents to prevent missing key change-related content.
How to set the configuration
This configuration is formulated based on the interface and parameter logic of FastGPT v4.17.0, and is adapted to the business data characteristics of this category.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the information density per chunk of documents such as ECN/ECR and process validation reports, avoiding splitting core change-related content |
| Overlap Length | 100–200 characters | Retains context for key fields such as cross-chunk version numbers and material codes, preventing loss of change-related information during retrieval |
maxToken | 8192 | Adapts to the input limit of mainstream open-source vectorization models, ensuring that long text slices related to changes can be fully vectorized |
| Similarity Threshold | 0.72–0.85 (calibrated via actual testing) | Filters low-relevance historical change documents, focusing on change content matching current bidding requirements |
| Single Search Limit | Top 10 entries | Controls the number of retrieval results to avoid context overload, while covering most change-related document types |
defaultToken | 512 | Sets the default input token length for the vectorization service, adapting to batch processing needs of sliced documents |
Three Common Mistakes
- Symptom: The vectorization service returns a format error prompt, or the interface shows slice processing timeout. Cause: Batch slices are not submitted according to the parameters configured in
defaultToken, only a single text slice is passed for vectorization, failing to adapt to the batch processing requirements of the vectorization service. - Symptom: Retrieval results include a large number of irrelevant historical change documents, failing to accurately match the current bidding change requirements. Cause: A reasonable similarity threshold is not set, or vector matching of core business fields such as version numbers and material codes is not prioritized, leading to insufficient retrieval accuracy.
- Symptom: After incremental index updates, old version change documents are still retrieved, creating a risk of mixing old and new versions. Cause: No version filtering logic is configured for the index, and version identifier fields are not included in retrieval association dimensions, making it impossible to distinguish between old and new change versions.
How to Confirm the Configuration is Correct
- Check the running logs of the vectorization service to confirm whether there are submission records for batch slices, and verify whether the number of slices processed per batch meets the configured requirements.
- Enter a search term containing a version number or material code, and check whether retrieval results preferentially return change documents associated with this business field.
- Perform an incremental index update operation, check the time taken for index updates, and confirm that old version change documents are not incorrectly retained in retrieval results.
- Adjust the value of the similarity threshold, observe changes in the number of retrieval results, and confirm that the threshold configuration can filter out low-relevance historical documents.
The values provided are common starting points and should be evaluated against the reader's own samples.