What This Category of Data Looks Like
This category of data primarily originates from steel coil labels and quality certificates, punch and feeder operation logs, mold management systems, MES systems, and coordinate measuring machine or fixture inspection data. Daily documents include stamping work instructions, material utilization rate sheets, mold inspection checklists, first/last piece records, dimensional inspection reports, and mold change logs. Data updates trigger per steel coil, per production batch, and each mold change operation. Document fields include mold number, stamping beat, material thickness, dimensional deviation values, mold change duration, and other related fields, with attached units such as millimeters, counts, and hours.
Constraints on Vector Models and Indexes
This category of data has high update frequency and fine granularity. Indexes must support incremental updates to accommodate new data from each coil, batch, and mold change. Documents contain both structured tables and unstructured text, so mixed-format chunking and vectorization processing are required. Data fields include dedicated units; vector modeling must retain the association between fields and units to prevent semantic confusion. Data from different mold change batches has significant variation. Index retrieval must prioritize matching documents linked to the current production batch to reduce interference from irrelevant results.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the paragraph and table row lengths of stamping documents, avoiding truncation of critical parameters such as mold numbers and material specifications during chunking |
| Chunk Overlap Length | 100–150 characters | Retains associated information such as mold numbers and mold change times across chunks, preventing context breaks |
defaultToken | 2000 token | Matches the average text length of a single stamping document, ensuring complete vectorization of core business content |
maxToken | 4000 token | Limits the total length of a single context, avoiding exceeding the call limits of external models |
| Similarity Threshold | 0.72–0.78 | Filters low-match results, focusing on valid content related to mold inspection and maintenance operations |
| Single Search Limit | Top 10 results | Adapts to the high-frequency query scenarios of workshop inspections, controlling the amount of returned information and response speed |
Three Common Misconfigurations
- Issue: Knowledge base index creation fails when deploying a PG database via Docker, on an 8c16G non-GPU virtual machine host. Cause: The PG vector extension plugin is not configured, or the chunk length is set too large, causing the single index write data volume to exceed the memory threshold.
- Issue: Timeout errors occur when calling vector models via external APIs. Cause:
maxTokenis not adjusted to fit the context limits of the external model, causing the request content to exceed the model's supported range and trigger a timeout. - Issue: Fields such as material thickness and dimensional deviation are empty in retrieval results after importing Feishu multi-dimensional documents. Cause: The association between structured table fields and units is not retained during chunking, leading to loss of critical business information during vectorization.
How to Confirm Proper Configuration
- Upload a single stamping work instruction, and check whether the chunked text retains core fields such as mold numbers and material specifications.
- Initiate a query for "How to handle mold wear", and verify that the similarity scores of returned results fall within the preset threshold range.
- Add a new mold change record document, trigger incremental indexing, then initiate a related query again to confirm the new document has been correctly retrieved.
- Confirm that the current FastGPT version is v4.17.0 to ensure compatibility of configuration items.
The values provided are common starting points and should be measured against the reader's own samples.