What the Data for This Category Looks Like
This category covers business related to painting lines for automobiles, home appliances, construction machinery, and metal furniture. Daily documents include painting process cards, paint batch sheets, paint mixing records, film thickness and color difference reports, oven curves, defect repair orders, and VOCs ledgers. Data sources include TDS/SDS documents from paint suppliers, real-time collected data from spray booths and ovens, colorimeters, film thickness gauges, environmental monitoring equipment, and MES systems. New records are generated per paint batch and each shift; some monitoring data is collected continuously.
What Constraints These Characteristics Impose on the Vector Models and Indexes Stage
Mixed document types create differences in text structure. This requires indexes to adapt to both structured fields and unstructured text, and support unified index storage for multiple types of coating business data. Frequently updated paint batch records, shift records, and continuously collected device data require indexes to support incremental updates, without full reindexing. Coating-related fields include professional parameters with units such as film thickness, color difference, and VOC concentration. Vector models must accurately understand such text with quantitative information, and support adaptation to domestic open-source vector models. Bidding scenarios require rapid matching of process windows and quotation basis. Index recall results must cover key business fields, avoiding omission of core parameters.
Configuration Settings
The following configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Ensures semantic integrity for long texts such as painting process cards and oven curves, avoids cutting critical process parameters |
maxToken | 4096 token | Matches the input limit of most open-source vector models, accommodates complete semantics of a single piece of coating document, and adapts to the token requirements of domestic models |
| Similarity Threshold | 0.72–0.85, calibrated via actual testing | Filters low-match paint batch or process records, adapts to professional semantic differences in coating business |
| Single Search Limit | Top 10 results | Controls the number of returned results, avoids excessive redundant information during bid document preparation |
defaultToken | Adjust based on actual document length | Adapts to tokenization needs of different document types, ensures semantic integrity of short texts such as paint mixing records and VOCs ledgers |
| Maximum Context | 8000 characters | Integrates information from multiple relevant documents, supports multi-dimensional basis matching for bid quotations |
Three Common Configuration Mistakes
- Phenomenon: A large number of irrelevant paint batch records appear in retrieval results, and returned results with matching degrees below the preset threshold are not filtered. Cause: The similarity threshold is not adjusted for professional coating semantics, and general thresholds lead to invalid matching.
- Phenomenon: After adding new paint batch records via an external system such as MongoDB, retrieval cannot recall the records. Cause: The synchronization mechanism between the vector database and the external system is not configured, and only the built-in index process of FastGPT is relied on.
- Phenomenon: A 400 error is returned when calling the vector model, prompting that the input token exceeds the limit. Cause: The
maxTokenparameter is not set reasonably, and overly long oven curve texts are directly passed to the model without adapting to the model's token limit.
How to Verify Proper Configuration
- Upload a complete painting process card, check that the chunked text retains complete process parameters and units, with no truncation.
- Initiate a retrieval for "spray process requirements for a batch of epoxy paint", verify that the number and matching degree of returned results meet the preset threshold.
- Add a test paint mixing record, check that the index completes incremental update automatically, and the record can be recalled normally during retrieval.
- Call the tokenization tool associated with the
defaultTokenparameter, verify that short texts such as VOCs ledger entries can be fully tokenized.
The values provided are common starting points and should be measured against the reader's own samples.