This document covers the vector model and indexing configuration workflow for FastGPT v4.17.0.
What Data Looks Like for This Category
Core data for welded structural component manufacturing comes from design drawings, material quality certificates, welding equipment parameters, filler metal inventory, non-destructive testing systems, and personnel qualification ledgers. Daily generated documents include welding procedure qualification records, welding procedure specifications, filler metal usage logs, welder qualification records, weld inspection reports, and repair orders.
Data is updated per weld, per batch, or per shift. Single records include fields such as weld number, filler metal grade, welder certification number, and test results. No unified fixed format template exists for these records.
What Constraints These Characteristics Impose on Vector Models and Indexing
Multi-source scattered data requires indexing to support batch import of multi-format files and unified field mapping. High update frequency requires an incremental indexing synchronization mechanism. This avoids resource consumption from full index rebuilds.
Documents include rigidly constrained procedure qualifications and qualification records. Chunking must retain key fields without truncation. Industrial domain terminology requires vector models adapted to welding-related vocabulary. Recall results must accurately match compliance verification needs for bid documents.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the core information length of documents associated with a single weld, avoids splitting that disrupts welding process logic |
| Overlap Length | 100–150 characters | Retains process connection details between adjacent chunks, prevents critical welding sequence truncation |
| Similarity Threshold | 0.75–0.85 | Filters low-relevance recall results, ensures accurate matching of filler metal and welder qualification information |
maxToken | Set to the selected embedding model/service maximum supported input tokens | maxToken is the input-token maximum supported by the selected embedding model/service; verify it in that provider configuration. See the v4.17.2 model-field reference. It is not retrieval/LLM context or a FastGPT-wide fixed cap. |
defaultToken | Calibrated via actual testing | Adapts to data length differences across batches of filler metal usage logs, optimizes index loading efficiency |
| Single Search Limit | Top 8 results | Controls recall result volume, adapts to multi-document verification needs for bid documents |
Three Common Misconfigurations
- Index status always displays as unready, and no retrieval requests can be initiated. The root cause is that vector embedding and index construction for the full dataset have not finished. Wait for the system to complete the incremental or full synchronization process.
- Doubao-embedding cannot be selected as the vector model. The root cause is that API call configuration for this model has not been entered in the platform. Complete the model access process first.
- Retrieval results do not link to the specific location of the corresponding source document. The root cause is that document metadata was not retained during chunking, or the traceability function was not enabled in the index configuration.
How to Verify Successful Configuration
- Import a single welding procedure qualification document, and confirm that the index generation progress updates normally.
- Run a test query containing filler metal grade and welder certification number, verify that the relevance of recall results meets expectations.
- View the source document information of retrieval results, confirm that traceability data such as file name and chunk position is included.
- Adjust configuration parameter values, regenerate the index, and verify that retrieval results match the expected changes from the adjustment.
The values provided are common starting points and should be measured against the reader's own samples.