What data looks like for this category
This category of data comes from raw material quality certificates, heating furnace systems, press collection systems, MES, non-destructive testing equipment, and customer forging drawings. Daily documents include forging process specifications, cutting cards, heating furnace logs, forging process cards, mold life records, heat treatment work orders, and ultrasonic testing reports. Data is updated per forging piece, batch, or furnace heat. Fields include forging ratio, fiber direction, furnace batch number, forging dimensions, heat treatment parameters, and more, using industrial standard units such as millimeters, degrees Celsius, and tons.
What constraints these characteristics impose on the vector models and indexing link
Data for this category is a mixed multi-source set, including structured system-collected fields and unstructured process documents. Update frequency is high, and single-batch data has strong correlation. There are many industry-specific terms, which requires the semantic understanding accuracy of vector models. Single records are associated with identifiers such as furnace batch numbers and forging batches, so associated fields must be retained during indexing to support subsequent summary work. In high-frequency update scenarios, the incremental synchronization logic of indexes must adapt to the short-cycle update rhythm to avoid performance loss caused by full reconstruction. At the same time, defaultToken and maxToken must be set appropriately to meet the context requirements of multi-document splicing.
How to set the configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the paragraph length of documents such as forging process specifications and process cards, avoiding splitting that disrupts process logic |
| Overlap Length | 100–200 characters | Retains the correlation of process parameters across chunks, preventing key steps from being lost due to splitting |
| Similarity Threshold | Calibrated based on actual testing | Adapts to the semantic matching accuracy of industrial terms, and must be adjusted based on actual retrieval results |
| Single Search Limit | Top 10 results | Matches the volume of associated data per furnace heat or batch, avoiding redundant recall |
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 | 8192 | Supports vector recall and summary calculation for batch ledger data, covering document content for large batches |
Three common mistakes
- The knowledge base upload status gets stuck in indexing. The cause is failing to adapt the incremental synchronization configuration for multi-source data, and not setting a reasonable timeout threshold, which prevents large furnace heat data from completing vectorization.
- Index matching results are incorrect after vectorization is complete. The cause is setting the chunk length too long, which disrupts the semantic integrity of short documents such as cutting cards and heating furnace logs, leading to vector matching deviations.
- Search result merging logic fails. The cause is not enabling index binding for batch-associated fields, which prevents ledger data from different furnace heats from being aggregated by batch, making it impossible to generate corresponding reports.
How to confirm the configuration is correct
- Upload a single heating furnace log document, check if the chunked text retains complete key parameters such as furnace temperature range and holding duration.
- Trigger batch ledger vectorization, verify that the indexing progress completes within a reasonable time frame, with no persistent stuck status.
- Enter a forging batch number for retrieval, confirm that the returned results include associated documents such as cutting cards and ultrasonic testing reports for the corresponding batch.
- Test the search result merging function, verify that ledger data for the same furnace heat is automatically aggregated by batch.
The values given are common starting points and should be measured against the reader's own samples.