What data for this category looks like
Data for this category comes from raw material batch ledgers, workshop MES systems, slurry and coating testing equipment, dew point monitoring devices, formation and grading cabinets, and laboratory testing equipment. Daily documents include batching records, coating and rolling parameters, winding or lamination process records, liquid injection ledgers, formation and grading data, and OCV and safety test reports. Data updates occur in real time per batch and per individual cell. Requirements for moisture content, cleanliness, and batch consistency are extremely high. The post-stage formation and grading link has a long data generation cycle.
What constraints these characteristics impose on vector models and indexes
Real-time, high-frequency per-cell and per-batch data creates index update pressure. Incremental indexing is required to reduce the load of full index rebuilding. Multi-dimensional process parameters and test data have complex fields. Fine-grained chunking is needed to preserve parameter units and process association logic. Electrochemical systems are sensitive to outliers. Vector embedding must retain key threshold information to avoid fuzzy processing. Long-cycle formation and grading data increases single document length. Reasonable chunk boundaries must be controlled to preserve data association. Data volume grows rapidly as production capacity expands. Horizontally scalable index structures are required.
How to set configurations
Configurations can set the default embedding token length via defaultToken, and limit the maximum token count per document via maxToken. This guide is adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the field density of single-cell process records, avoid losing parameter associations after splitting |
| Overlap Length | 100–150 characters | Preserve process parameter context across chunks, prevent critical logic from being cut off at chunk boundaries |
| Similarity Threshold | 0.72–0.85 | Match the feature similarity range of cell process data, filter low-correlation abnormal matches |
| Single Search Limit | Top 8 entries | Adapt to multi-parameter association queries for batch data, control redundancy of returned results |
| Maximum Context Token Count | 4096 token | Adapt to the long text length of formation and grading data, ensure complete embedding of key curve information |
| Query Optimization | Calibrated via actual testing | Adapt query intentions for different process scenarios, avoid generic optimization damaging the semantics of professional fields |
Three common errors
- Symptom: Knowledge base search response times out, logs show vector retrieval takes longer than 60 seconds. Cause: Incremental indexing is not configured, and full index rebuilding processes real-time data for every batch, leading to excessive index loading pressure.
- Symptom: Some process parameter queries return no matching content. Cause: Chunk length is set too small, splitting cuts off the association logic between parameters and corresponding test results, leading to loss of key association information during vector embedding.
- Symptom: When using the shaw/dmeta-embedding-zh model, the proportion of outliers in retrieval results is too high. Cause: No preprocessing is done for sensitive thresholds of electrochemical data, and vector embedding blurs the weight of key parameters such as moisture content and internal resistance.
How to confirm configurations are properly set
- Manually import batching records and formation and grading data for a single batch, check if chunked documents retain complete parameter units and associated fields.
- After configuring incremental indexing, submit real-time generated cell test data, confirm that the index can complete updates within 5 minutes.
- Select the shaw/dmeta-embedding-zh model, query known abnormal process parameters, check if the match rate of retrieval results meets the preset threshold.
- View the system index monitoring panel, confirm that the number of returned entries per search does not exceed the preset single search limit.
The values provided are common starting points, and should be evaluated against the reader's own samples.