What this category of data looks like
This category covers daily work outputs of manufacturing enterprise IPQC, FQC, quality engineering and laboratory teams. Sources include MES systems, test equipment data, laboratory records, process parameter documents, customer standard documents and historical defect databases. Data updates synchronize with each production batch and each shift’s patrol inspection, and real-time process and inspection data are collected continuously. Typical documents include control plans, patrol inspection records, SPC reports, finished product inspection reports, nonconforming product review records, release orders and quality monthly reports. Fields cover inspection items, judgment results, defect types, release permission identifiers and more. There are no unified fixed units, and they vary by document type.
What constraints do these characteristics impose on the "vector models and indexes" link
Data sources for this category are scattered, update frequency is high, and document types are diverse. This requires the indexing link to support incremental updates to adapt to real-time data synchronization per production batch and shift, avoiding the time cost of full index rebuilding. The length of different documents varies significantly, from short text like release orders to long SPC analysis content in reports. A chunking strategy is needed to adapt to text units of different lengths. At the same time, the data includes business fields such as permission identifiers and defect codes. The index must retain field associations to support precise matching for subsequent Q&A, and must support both structured data exported from test equipment and unstructured content such as manually filled patrol inspection records.
How to set the configurations
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-paragraph text length of patrol inspection records and SPC reports for this category, avoiding semantic fragmentation or reduced vector accuracy |
| Overlap Length | 100–150 characters | Ensures semantic coherence between adjacent chunks, covering cross-chunk inspection item association content |
| Similarity Threshold | 0.75–0.85 | Filters low-match historical defects and patrol inspection records, avoiding incorrect association of unrelated quality data |
| Maximum Context | 3000–4000 tokens | Adapts to long-text Q&A scenarios such as release review and nonconforming product review, ensuring complete context |
defaultToken | 8192 | Adapts to the input length limit of mainstream vector models, compatible with the total token count of most documents in this category |
| Single Search Limit | Top 5 entries | Controls the redundancy of returned results, focusing on the most relevant quality documents and historical defect data |
Three common mistakes
- Issue: After upgrading to FastGPT v4.17.0, indexing of existing CSV knowledge bases fails, and the interface returns an error message about parameter verification failure. Cause: The new version updates the verification rules for parameters such as chunk length and
defaultToken. The values of the original configuration exceed the adaptation range of the new version, or the encoding format of the CSV file does not adapt to the new version’s import logic. - Issue: The knowledge base indexing progress stalls, and uncompleted index data cannot automatically advance to the completed state. Cause: No breakpoint-continuing indexing strategy is configured. A full index process triggers a timeout due to excessive batch data, or processed chunk records are not retained after an interruption.
- Issue: The vector model called during knowledge base reference Q&A does not match the configured one, and the specified model cannot be switched. Cause: The access parameters of the target vector model are not updated in the system settings, or the index link does not regenerate vector embeddings for the corresponding model, still using vector data from the old model.
How to confirm the configuration is correct
- Upload a single typical document, such as a finished product inspection report, manually trigger chunk preview, and confirm that the chunk and overlap configurations meet preset requirements, with no excessive truncation or semantic fragmentation.
- View the index operation log, confirm that only newly added or modified quality document data is synchronized, and no already indexed content is processed repeatedly, aligning with the incremental update configuration logic.
- Enter the system vector model configuration page, verify that the currently bound model parameters match business requirements, and confirm that the access information is configured correctly.
- Initiate a simulated business Q&A, confirm that the returned matching results include typical document content for this category, and the number of returned results meets the preset single search limit requirement.
The values provided are common starting points and should be measured against the reader's own samples.