What this category's data looks like
Data sources for welded structural component manufacturing include design drawings, material quality certificates, welding equipment parameters, welding material inventory, non-destructive testing systems, and personnel qualification ledgers. Daily generated documents cover welding procedure qualification records, welding procedure specifications, welding material consumption records, welder qualification records, weld inspection reports, and repair orders. Data updates follow a rhythm per individual weld, per batch, or per shift. Each single data entry links to content such as a unique weld identifier, base material grade, welding material batch number, welding parameters, inspection results, and welder qualification number. No fictional statistical values are included.
Constraints on vector models and indexing
The high update frequency and multiple associated dimensions of data for welded structural component manufacturing impose multiple constraints on the vector model and indexing workflow. First, single data entries link to multiple independent entities such as welds, welding materials, and welders. Chunking must retain entity association information to avoid index breaks that cause traceability failures. Second, frequently updated weld data requires indexes to support incremental synchronization, avoiding excessive resource consumption from full index rebuilds. Third, quality traceability requires precise matching of entity associations. Indexes must support multi-field combined retrieval; full-text matching alone cannot meet accuracy requirements. Additionally, rigid requirements for special equipment acceptance ensure that vector retrieval results can be directly traced back to original data sources.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the valid single-paragraph information length of documents such as welding procedure specifications and weld inspection reports, avoids chunking that breaks entity associations |
| Overlap Length | 50–100 characters | Retains key associated information such as weld numbers and welding material batch numbers across chunks, prevents entity breaks |
| Similarity Threshold | 0.75–0.85 | Adapts to the vector matching accuracy of structured data such as welding parameters and inspection results, filters low-correlation retrieval results |
| Single Search Limit | Top 10 entries | Matches the volume of associated data required for single weld traceability, avoids redundant information interfering with defect analysis |
defaultToken | 1000–1500 token | Adapts to the vector encoding limit of FastGPT v4.17.0, covers the complete information of short documents such as batch welding material consumption records and welder qualification records |
| Maximum Context | 4000–6000 characters | Covers all associated data for a single weld, supports complete defect cause traceability analysis |
Three common configuration errors
- A 401 status code is returned when calling the vector model, and the interface prompts authentication failure. The cause is failure to correctly configure the API key for the domestic vector model, or the key permission has not been granted for the corresponding interface.
- Duplicate weld inspection report entries appear after index merging, with multiple reports of the same number in retrieval results. The cause is failure to enable index deduplication configuration, or failure to use the weld number as the unique identification field for deduplication.
- When using ollama to deploy a vector model, FastGPT calls are rejected but curl tests pass, and the interface returns a call failure log. The cause is that FastGPT's vector model request does not correctly carry ollama's local access authentication parameters, or the port configuration does not match the ollama deployment port.
How to confirm the configuration is correct
- Upload a single welding procedure specification document, check if the chunked fragments retain key associated information such as weld numbers and welding material batch numbers, adjust chunk and overlap lengths to meet expectations.
- Submit a search query containing a weld number, verify that the number of retrieval results matches the single search limit configuration, and all results are associated with relevant data for the target weld.
- Import batch-updated welding material consumption records, check if the index automatically synchronizes new data, with no full rebuild trigger records.
- Call the vector model interface, check that the returned vector encoding length matches the value range configured for
defaultToken, with no truncation prompts.
The values provided here are common starting points and should be evaluated against the reader's own samples.