What This Category’s Data Looks Like
This category’s data primarily comes from design drawings, material quality certificates, welding equipment parameters, filler metal inventories, non-destructive testing systems, and personnel qualification ledgers. Daily documents include welding procedure qualifications, welding procedure specifications, filler metal issue records, welder qualification records, weld inspection reports, and repair orders. Data updates follow the rhythm of each weld, batch of material, or shift operation. Individual records contain fields such as base material grade, filler metal model, welder qualification number, non-destructive testing results, and number of repairs, and use industry-standard units matching the record type.
What Constraints Do These Characteristics Impose on Knowledge Base Retrieval and Recall
High-frequency, fine-grained data updates require the retrieval system to support frequent incremental updates, to avoid excessive time spent on full reindexing. Diverse and highly specialized document types demand domain-specific semantic understanding capabilities, to prevent irrelevant content from being retrieved. Fields within individual records are closely related, so retrieval must match multiple conditions such as base material, filler metal, and welder qualification at the same time. Relying solely on a single keyword cannot achieve effective retrieval. Records such as non-destructive testing results and repair orders are directly linked to quality risks. Retrieval results must prioritize high-priority fields, and the accuracy of recalled results must be ensured to avoid missing key constraint items.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Documents in the welded structural component field are highly specialized, so low-match irrelevant content must be filtered to ensure domain relevance of recalled results |
limit | Top 8–12 results | The number of process, qualification, and inspection records linked to a single batch of welds or single workstation is limited; too many results will exceed the context window limit |
usingReRank | Enabled | There are many specialized domain terms, and re-ranking can further filter recall results with semantic deviations and improve the accuracy of key information retrieval |
datasetSearchUsingExtensionQuery | Enabled | User quality audit questions can be expanded into multi-dimensional queries including base material, filler metal, and welder qualification, to match records linked across multiple fields |
collectionIds | Assign corresponding knowledge base collections by business domain | Separate welded structural component-specific documents from other category data to avoid cross-category interference. This configuration is designed based on the knowledge base retrieval function of FastGPT v4.17.0 |
Three Common Mistakes
- Phenomenon: A large number of machining documents unrelated to welding processes appear in retrieval results. Cause: The
collectionIdsconfiguration to limit the dedicated knowledge base collection was not set, and document data from other categories was mixed in. - Phenomenon: The results returned by semantic search do not match filler metal models to base material grades. Cause:
datasetSearchUsingExtensionQuerywas not enabled, so multi-field linked queries were not expanded, and only single keyword matching was relied on. - Phenomenon: Retrieval returns empty results or times out. Cause: The
limitparameter was not adjusted, so too many redundant records were recalled exceeding system load, or thesimilaritythreshold was set too high resulting in no matching results.
How to Confirm the Configuration Is Correctly Set
- Submit a quality audit query that includes base material grade and filler metal model, and check whether the corresponding welding procedure specification documents are included in the recalled results.
- View the retrieval log to confirm that only the specified knowledge base collection was called, and no document data from unrelated categories was loaded.
- When adjusting the
similaritythreshold, compare result relevance across different thresholds to select a value range that meets business audit requirements. - After enabling the re-ranking function, verify whether the semantic matching accuracy of the top 3 recalled results is higher than that of results when the function is disabled.
The values provided here are common starting points and should be measured against the reader's own samples.