This document is written based on FastGPT v4.17.0.
What This Category’s Data Looks Like
Data for this category primarily comes from daily documents of professional heat treatment plants and heat treatment workshops in automotive and bearing component factories. These documents include heat treatment process cards, charging charts, furnace temperature curves, hardness and metallographic reports, equipment inspection forms, and abnormal disposal records. Data sources cover material grades, customer technical requirements, temperature control instruments, furnace temperature uniformity testing equipment, hardness testers and metallographic equipment, and MES systems. The data update frequency is per heat load and per batch. Each individual document contains structured and unstructured fields including temperature values (unit: ℃), hardness values (unit: HV), metallographic structure descriptions, inspection items, and abnormal records.
Constraints Imposed on Vector Models and Indexing
These data characteristics impose multiple constraints on the vector model and indexing workflow. First, high data update frequency and multiple documents linked to each heat load require indexes to support incremental updates and batch associated indexing. Second, documents contain long time-series furnace temperature curves and professional metallographic descriptions, requiring chunking strategies that preserve long text semantic integrity. Third, fields include professional engineering parameters and discrete abnormal records, requiring metadata extraction to cover key identifiers such as furnace number and material grade. This prevents loss of business association information during recall. Fourth, the strong association between abnormal disposal records and process cards requires indexes to support cross-document semantic associated recall.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the long text length of heat treatment process cards and furnace temperature curves, avoid semantic fragmentation |
| Overlap Length | 100–150 characters | Cover cross-chunk associated information for charging charts and furnace temperature curves, preserve time-series continuity |
| maxToken | 8192 token | Match the maximum input limit of bce-embedding-v1, adapt to long text content of metallographic reports |
| defaultToken | Calibrated based on actual testing | Adapt to short text fields in heat treatment equipment inspection forms, optimize vector generation efficiency for small-scale data |
| Similarity Threshold | 0.72–0.80 | Filter low-match association results for abnormal disposal records and process cards, improve recall accuracy |
| Single Search Limit | Top 6 entries | Cover multi-dimensional inspection and maintenance data for a single heat load, avoid interference from redundant information |
Three Common Misconfiguration Scenarios
- After uploading chunk-mode data via the pushdata API, the interface continuously displays an "Indexing" status without updates. This occurs when the index trigger timing is not configured to synchronize with heat load updates. The incremental indexing polling interval does not align with the data generation rhythm per heat load, leading to data accumulation without completed indexing.
- When calling the embedding model, an error prompt "This token does not have permission to use the model" is returned. This happens when the key permission corresponding to
defaultTokenis not correctly configured, and the usage permission for the selected embedding model is not bound. This results in interface call interception. - After entering a device abnormal maintenance question, the recall results do not include the furnace temperature curve for the corresponding heat load. This occurs when metadata extraction rules are not configured, and furnace number and material grade are not used as index association fields. This leads to recall results disconnected from the business scenario.
How to Verify Proper Configuration
- Upload a single complete furnace temperature curve document. Check whether the index completion status is finalized within a reasonable time frame, with no continuous accumulation prompts.
- Call the embedding test interface, enter a segment of heat treatment process card text. Verify that the returned vector data format meets requirements, with no truncation errors.
- Enter the question "Hardness abnormality for a certain heat load". Check whether the recalled documents include the inspection form and abnormal disposal record for the corresponding furnace number.
- Check whether the
maxTokenparameter matches the input limit of the selected embedding model. This avoids long text truncation leading to semantic loss.
The values provided are common starting points and should be measured against the reader's own samples.