What Data for This Category Looks Like
Data for this category comes from material grade archives, customer technical requirement documents, temperature control instrument collection data, furnace temperature uniformity test reports, output results from hardness testers and metallographic equipment, and factory MES systems. Daily generated documents include heat treatment process cards, furnace loading diagrams, furnace temperature curves, hardness and metallographic reports, equipment inspection forms, and exception handling sheets. Data updates occur per furnace load or production batch. Individual documents contain clear process parameters, test values, and operation record fields, and some documents include time-series temperature change data.
Constraints Imposed on Vector Models and Indexes
The high-frequency, small-batch updates, multi-source heterogeneous document structure, and time-series data characteristics of this category impose multiple constraints on the vector indexing process. Real-time data updates per furnace load require indexes to support incremental synchronization, avoiding delays from full index reconstruction. Time-series furnace temperature curve data requires retaining local temporal associations during chunking, to avoid breaking the logical continuity of temperature changes through splitting. Format differences across multi-source data require vector models to adapt to semantic mapping for different fields, while indexes need to support fast retrieval of mixed-type data. Severe consequences of errors in single furnace load data also require index recall accuracy to match the needs of business audits.
Configuration Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-segment semantic integrity of heat treatment process cards and test reports, avoiding splitting that breaks associations between process parameters |
| Overlap Length | 100–150 characters | Retains temporal cross-chunk associations for furnace temperature curves, preventing temperature change logic from being disrupted by chunking |
defaultToken | 8192 | Adapts to the total token length of multi-segment process parameters and test reports, meeting vector embedding requirements for long documents |
maxToken | Set to the selected embedding model/service maximum supported input tokens | maxToken is the input-token maximum supported by the selected embedding model/service; verify it in that provider configuration. See the v4.17.2 model-field reference. It is not retrieval/LLM context or a FastGPT-wide fixed cap. |
| Similarity Threshold | 0.72–0.85 | Matches semantic similarity judgments for heat treatment exception data, filtering low-correlation historical records |
| Single Search Limit | Top 8 entries | Balances retrieval efficiency and the coverage of historical data required for audits |
| Maximum Context | 4000 characters | Limits the context length passed to the large model, avoiding exceeding the model's processing limits |
Three Common Misconfigurations
- 401 authentication failure errors occur when the vector model access key is configured incorrectly, or the custom channel request path does not match the model's official interface specifications.
- Automatic deletion of duplicate document chunks in the knowledge base leads to disordered index order. This happens when the system's default duplicate chunk deduplication switch is not disabled, so the custom chunk order from splitting is not preserved.
- Slow index construction speed occurs when reasonable chunking parameters are not configured, or the parallel processing mechanism for batch index construction is not enabled, leading to an excessively large single-batch data processing volume.
How to Verify Proper Configuration
- Import a single heat treatment furnace temperature curve document, check that the number of chunks after splitting matches expectations, and there is no temporal data breakage across chunks.
- Attempt to connect to the specified vector model, initiate a retrieval request, and confirm that the returned status code is 200 and there are no authentication errors.
- Enable the duplicate chunk retention switch, import a document that has been custom split, and check that the order of chunks in the knowledge base matches the original order after splitting.
- Simulate batch import of multiple furnace load data, confirm that the index construction process has no timeouts or abnormal resource usage.
The values provided here are common starting points, and should be evaluated against the reader's own samples.