What data for this category looks like
This category covers end-to-end ledgers for professional heat treatment plants and heat treatment workshops in automotive and bearing component factories. Daily documents include heat treatment process cards, charging charts, furnace temperature curves, hardness and metallographic reports, equipment inspection forms, and exception handling sheets. Data sources include material grades, customer technical requirements, temperature control instruments, furnace temperature uniformity testing equipment, hardness testers and metallographic equipment, and MES systems.
Update frequency is per furnace charge and per batch. Single record includes fields such as furnace number, batch number, heating temperature, holding time, cooling rate, hardness value, and metallographic structure type, with standard units including degrees Celsius, minutes, HV, HRC, and others.
What constraints do these characteristics impose on the vector model and indexing workflow
The data characteristics of this category impose multiple constraints on the vector model and indexing workflow. First, high-frequency per-furnace updates require indexes to support incremental triggering, avoiding resource consumption from full reconstruction. Second, mixed document types (tabular process cards, curve-based furnace temperature data, text-based exception handling sheets) require chunking strategies adapted to different document structures, to avoid breaking logical associations during splitting. Third, fields with specific units (such as HV hardness values, temperature in degrees Celsius) require retaining unit associations during vector modeling, to prevent incorrect matching of similar data with different units. Finally, unstructured content in exception handling sheets requires precise chunking, to avoid mixing exception information from different furnace charges.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the logical integrity of single segments of heat treatment process cards and furnace temperature curve text, avoid splitting process steps |
| Overlap Length | 100–150 characters | Retain associated information such as furnace number and batch number across chunks, ensure vector indexes for data from the same furnace charge can be correctly correlated |
| Similarity Threshold | 0.75–0.85 | Distinguish subtle differences in process parameters between different furnace charges in the same batch, avoid recalling irrelevant data |
| Single Search Limit | Top 8 entries | Cover multiple types of documents associated with a single furnace charge, such as process cards, furnace temperature curves, and hardness reports |
defaultToken | Set from representative sample documents | defaultToken is the default token count for direct text chunking; tune it on representative source documents. See the v4.17.2 model-field reference. It is not a vector dimension, embedding-model input cap, or retrieval/LLM context. |
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. |
Three common errors
- After uploading data using the chunk mode pushdata API, the interface remains in the "Indexing" status for an extended period. Cause: The high-frequency update rhythm of heat treatment data is not adapted, and the index triggering threshold is set too high, resulting in a backlog of incremental data that does not trigger actual index construction.
- The last set of indexes after question splitting fails validation. Cause: Core associated fields such as furnace number and batch number are not retained during chunking, causing vector indexes to fail to match multiple types of documents from the same furnace charge, and the validation step determines the index is invalid.
- The search results return a large amount of process data from non-target furnace charges. Cause: The similarity threshold is set too low, making it impossible to distinguish differences in process parameters between different batches, recalling ledger information from unrelated furnace charges.
How to confirm the configuration is properly set
- Upload complete ledger data for a single furnace charge, check the FastGPT v4.17.0 index construction logs, confirm that the chunked data includes associated fields such as furnace number and batch number.
- Enter process parameter keywords for a single furnace charge, verify that the number of recalled search results matches the single search limit setting.
- Enter a batch summarization query for multiple furnace charges, confirm that no context overflow error appears in the generated report.
- Compare search results for the same batch and different batches, confirm that the similarity threshold can effectively distinguish the recall priority of the two types of data.
The values provided are common starting points and should be measured against the reader's own samples.