What Data Looks Like for This Category
Data for this category originates from multi-source systems of forging plants, aerospace and automotive forging suppliers. Sources include raw material quality certificates, heating furnace records, press collection data, MES systems, and non-destructive testing reports. Daily documents cover forging process specifications, cutting lists, mold life records, and heat treatment work orders. Core fields include furnace batch number, forging batch number, heating temperature, forming pressure, and defect type. Update frequency is once per piece, per batch, or per furnace run. Data includes both structured parameters and unstructured reports.
Constraints Imposed on Vector Models and Indexes
Mixed multi-source data formats require the index module to adapt to chunking rules for both structured parameters and unstructured documents. High-frequency updates based on furnace batches and batches require the index to support incremental triggering, avoiding full repeated scans. Strong correlations between core fields (such as furnace batch number and forging batch number) require the index to retain metadata associations, preventing feature fragmentation. The concentrated volume of historical data involved in a single analysis requires controlling the scale of recall results to ensure response speed.
Configuration Settings
The following configurations apply to the vector model and index module of FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the paragraph structure of forging process specifications and non-destructive testing reports, avoids excessively long single chunks that exceed model limits |
| Overlap Length | 100–150 characters | Retains key metadata such as forging batch numbers and furnace batch numbers across chunks, prevents feature breakage |
maxToken | 4096 token | Adapts to the input upper limit of mainstream embedding models, fully carries core business fields such as heating temperature and pressure parameters |
| Similarity Threshold | 0.75–0.85 | Distinguishes similar defect features such as folds and cracks, reduces false recall probability for defect analysis |
| Single Search Limit | Top 10 results | Adapts to the concentrated nature of forging batch data, avoids excessive results prolonging analysis time |
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. |
Three Common Misconfigurations
- Phenomenon: Index construction progress stalls at the last group with no clear error prompt. Cause: Incremental index rules triggered by furnace batch are not configured. The system repeatedly scans full forging batch data, causing resource blocking.
- Phenomenon: Knowledge base fails to complete indexing normally and gets stuck at the "building index" step. Cause: Chunking adaptation is not implemented for the multi-source format of forging data. Mixing structured MES data and unstructured PDF detection reports during import causes chunking logic conflicts.
- Phenomenon: Indexing can be completed after replacing a custom embedding model, but search tests return errors. Cause: The
defaultTokenconfiguration is not adjusted synchronously. The input token upper limit of the new model does not match the original configuration, causing vector query truncation exceptions.
How to Verify Proper Configuration
- Upload a single forging process specification document, check if chunked fragments fully retain key metadata such as furnace batch numbers and heating ranges.
- Trigger a batch indexing task, observe if the progress bar advances in an orderly manner by batch, with no long-term stagnation on a single group.
- Replace the test embedding model, perform defect feature search after completing indexing, confirm that returned results have no errors and matching degrees meet business expectations.
- Check system index logs, confirm that the
maxTokenconfiguration has no content truncation prompts, and all core business fields have been successfully vectorized and stored.
The values given are common starting points and should be measured against the reader's own samples.