What the data for this category looks like
The data for this category mainly 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 qualifications, welding process specifications, filler metal usage records, welder qualification records, weld inspection reports, repair orders, and similar items. Data updates are triggered per weld, per batch of materials, or per shift operation. Most individual documents contain structured fields and corresponding numerical units, such as welding parameters, qualification numbers, and inspection results.
What constraints these characteristics impose on the vector models and indexing link
High-frequency, fine-grained data updates require indexes to support frequent incremental updates, and avoid full reindexing that consumes excessive system resources. Multiple types of business documents are stored together, so classification index rules must be set by document type. This prevents vector features from different business scenarios from being mixed, which reduces retrieval accuracy. Documents include structured fields like welding parameters and qualification numbers, so the vector model must capture the associated features of numerical values and text. This avoids irrelevant matching results. Repair orders and inspection reports have high relevance, so cross-document associated retrieval must be supported. This ensures that process issues can be linked to corresponding rectification and inspection records.
Configuration Settings
This document is based on the configuration logic of FastGPT v4.17.0. Specific configuration items and recommendations are as follows:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Documents such as welding process specifications mostly contain continuous process step descriptions. This length can fully cover a single welding process and avoid semantic fragmentation |
| Overlap Length | 50–80 characters | Compensates for semantic gaps after long text chunking, and ensures that the relevant content before and after weld inspection reports can be fully retrieved |
| Similarity Threshold | Calibrated via actual testing | Must match the precise retrieval requirements for welder qualifications and welding parameters. Avoid missing detections caused by a threshold that is too high, or irrelevant matches caused by a threshold that is too low |
| Single Search Limit | Top 8 entries | Covers multiple types of documents related to a single weld, including process, qualification, and inspection documents. Prevents excessive results from interfering with core matching |
defaultToken | 4000 | Adapts to the context carrying capacity of long documents such as welding procedure qualifications, and ensures that associated document content can be fully called during Q&A |
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. |
Three Common Configuration Mistakes
- After connecting a local embedding model, the interface shows no response in the indexing section for a long time. The cause is that the local call port and permissions of the model are not configured, so the vector generation process cannot normally pull document data.
- An error occurs when using the embedding-2 model. The cause is that the OpenAPI channel authentication information of the model is not correctly configured, or the corresponding access interface is not opened during model deployment.
- Retrieval returns no results after customizing the index configuration. The cause is that the index rules are not bound to document types, so the content of welding process specifications is not matched to the corresponding index category.
How to Verify Correct Configuration
- Upload a single welding process specification document, check that the vector generation log shows no abnormal errors, and confirm that the model call is working normally.
- Submit a retrieval request that includes a welder qualification number, and verify that the returned results include the corresponding welder qualification record document.
- Submit batch repair order data, check that the index update progress is completed at the preset frequency, and no full reindexing is triggered.
- Adjust the similarity threshold and submit a retrieval request, observe the change in the matching accuracy of the returned results, and confirm that the threshold configuration takes effect.
The values provided are common starting points and should be evaluated against the reader's own samples.