What this category of data looks like
This category of data covers full-process records from the pole piece, assembly, formation, and quality departments of power, energy storage, and consumer battery enterprises. It includes batching records, coating and roller pressing parameters, winding or lamination records, liquid injection records, formation and grading data, OCV and safety test reports. Data sources cover raw material batch ledgers, MES systems, slurry and coating detection data, dew point monitoring records, real-time data from formation and grading cabinets, and laboratory test reports. Updates follow three schedules: production batch, single cell, and real-time metrics. Each data entry includes fields such as batch number, parameter value, detection time, and detection result. Some parameters have units, such as coating areal density g/㎡, dew point temperature ℃.
What constraints these characteristics impose on vector models and indexes
The multi-source real-time nature and batch-level update requirements of this category of data require vector indexes to support high-frequency incremental writes. This avoids performance loss caused by full index rebuilding. The field differences across multiple document types—such as numerical items for slurry ratios and text items for safety reports—require vector models to adapt to mixed-modal data encoding. Indexes must also support metadata filtering based on fields like batch number and detection time. Fine-grained data for single cells will increase index size. This requires controlling the dimension and storage overhead of individual vectors. It also ensures that vector encoding of time-series data such as formation and grading curves retains temporal features. This prevents loss of critical information during consistency checks.
How to configure settings
This document is written based on the configuration logic of FastGPT v4.17.0. Specific configuration items are listed below:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the length of single documents such as batching records and coating parameters. Prevents single chunks from crossing critical information boundaries |
| Overlap Length | 50–100 characters | Retains contextual information for coating and roller pressing parameters. Prevents chunks from breaking continuous process parameter sequences |
| Similarity Threshold | Calibrated based on actual testing | Must match the dispersion of battery cell detection data. Prevents incorrect matching of slurry ratio records across different batches |
| Single Search Limit | Top 10 entries | Controls the number of recall results. Adapts to batch data processing requirements for batch-level audits |
defaultToken | 4096 | Matches the long-text vector encoding requirements of formation and grading data. Ensures complete curve data is encoded correctly |
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 configuration mistakes
- Incorrect channel configuration when connecting a third-party vector model. The interface displays "model channel not enabled". This occurs because the key binding and channel activation for the third-party vector model are not completed in FastGPT system settings first.
- Failed connection to an Ollama-deployed BGE-M3 vector model. Retrieval returns empty results or the error "model connection timed out". This occurs because the API port of the vector model is not opened to the FastGPT deployment network, and the correct local access address is not filled in the configuration.
- No metadata filtering configured after enabling the index model. Retrieval results include cross-batch cell data. This occurs because metadata fields such as batch number and detection time are not bound. The index cannot perform precise recall based on production batches.
How to confirm the configuration is correct
- Upload a typical coating and roller pressing parameter document. Check that the vector generation task status shows "completed" with no error logs.
- Initiate a retrieval for a specific batch's slurry ratio. Verify that the recall result metadata includes the batch number, and the number of results matches the single search limit configuration.
- View the storage usage of the vector index. Confirm no abnormal expansion occurs, and matches the expected index scale for a single batch of data.
- Test retrieval of long-text formation and grading curve data. Confirm complete curve data is encoded correctly, with no truncation errors.
The values provided here are common starting points. They should be evaluated against the reader's own samples.