What the data for this category looks like
The core data for this category is generated and maintained by manufacturing enterprise change management teams. Sources include PLM, ERP, MES, supplier materials, validation tests, and customer-specific requirements. Daily documents include ECN/ECR, change assessment reports, drawings and BOM version files, process validation reports, transition plans, inventory disposal documents, and customer approval documents. Data updates do not follow a fixed cycle. They are synchronized each time a process or engineering change is initiated, and involve multiple types of content produced through cross-department collaboration.
What constraints these characteristics impose on the vector models and indexing link
Multi-source heterogeneous data sources require indexes to support unified vectorization and associated retrieval of cross-system documents. Non-fixed update schedules require indexes to adapt to incremental trigger modes, avoiding excessive resource usage from full reconstruction. Mixed document types require chunking rules that balance short-text change clauses and long-text BOM lists. Strongly associated attributes related to changes require retrieval to prioritize matching key fields such as version and approval status, preventing confusion between old and new versions. Additionally, the severity of potential errors requires index recall results to have high precision, avoiding missing key change-related information.
How to set the configuration
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-paragraph content length of ECN and process validation reports, avoiding splitting key change clauses and version transition points |
| Overlap Length | 100–150 characters | Retains change-related information across chunks, preventing loss of the correspondence between BOMs and drawings after splitting |
| Similarity Threshold | 0.75–0.85 | Distinguishes change content between old and new versions, avoiding mismatched old documents or irrelevant supplier materials |
| Single Search Limit | Top 10 entries | Covers associated change records from multiple sources such as PLM and ERP, ensuring sufficient cross-review basis is recalled |
defaultToken | 8192 | Accommodates long-text content such as BOM lists and drawing notes, meeting the storage requirements of a single context |
maxToken | 16384 | Supports indexing context for batch synchronization of multiple change documents, adapting to batch change scenarios |
Three common errors
- Knowledge base priority configuration does not take effect, and search results include content from multiple knowledge bases. The cause is failure to bind change documents to corresponding knowledge bases based on business attributes, and failure to enable cross-library priority filtering rules.
- Index reconstruction takes more than 60 seconds, and a timeout error is displayed on the interface. The cause is failure to enable incremental index mode, triggering full reconstruction of all historical change documents each time, exceeding the system's default processing time limit.
- Batch index addition requests return empty fields. The cause is failure to correctly carry collection identifiers and chunk metadata, causing the system to fail to identify the content of the change documents to be indexed.
How to confirm the configuration is complete
- Manually upload a test ECN document, check whether the chunked content after index generation includes complete change clauses and version information.
- Initiate a cross-source search request, verify that the results preferentially return content from the specified priority knowledge base, in line with preset retrieval rules.
- Trigger incremental index reconstruction, confirm that the time meets expectations, no timeout error occurs, and the status panel shows reconstruction completed.
- View the batch index addition return results, confirm that they include correct collection identifiers and chunk counts, with no abnormal field missing.
The values provided are common starting points and should be measured against the reader's own samples.