What data looks like for this category
Data for this category covers full-process outputs of battery packs, energy storage cabinets, and BMS systems. Daily documents include cell grouping rules, BOM lists, welding and torque records, BMS flashing logs, insulation and pressure resistance reports, EOL test data, and system configuration lists. Data sources include cell capacity grading data, MES systems, welding equipment, torque tools, BMS software version records, and EOL test bench data. Update cadence: A corresponding record is generated each time a PACK or energy storage cabinet production batch is completed, and associated data is updated synchronously each time the BMS software version is changed. Fields include cell parameters, welding torque values, flashing version numbers, test voltages, and more. There are no uniform standard units; values adjust based on the data type of different links.
Constraints on vector models and indexes
Multi-source heterogeneous data, high-frequency small-batch updates, and inconsistent field units impose multiple constraints on the vector model and index link. Mixed structured and unstructured data requires indexes to support vector fusion storage across data types. Records for a single PACK or energy storage cabinet are updated frequently, and full index reconstruction takes too long. This requires support for incremental index updates. The text length of data from different links varies widely, from short torque records to complete EOL test reports. A flexible chunking strategy is needed to adapt to inputs of different lengths. There are no uniform units for fields. Feature standardization processing must be completed before vector modeling to avoid unit differences affecting vector similarity calculations.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxToken | 8192 token | Long texts such as EOL test reports and system configuration lists for this category usually do not exceed this length, and it adapts to the input limits of mainstream vector models |
| Chunk Length | 800–1200 characters | Covers medium-length documents such as welding records and grouping rules, avoids vector distortion caused by overly long single chunks, and reduces excessive index redundancy caused by overly short chunks |
| Overlap Length | 100–200 characters | Ensures contextual coherence across chunks, and adapts to the front and back link data that needs to be associated during quality traceability |
| Similarity Threshold | 0.75–0.85 | Distinguishes valid associated data from noise, and adapts to the matching accuracy requirements of single-device data for this category |
| Single Search Limit | Top 10 results | Balances retrieval efficiency and recall coverage, and meets the need to quickly locate associated records for a single PACK or energy storage cabinet |
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 mistakes
- Phenomenon: The vectorization interface returns a parameter format error, and the log shows that only single text input is accepted. Cause: The chunked batch submission configuration is not enabled, and only single text slices are pushed to the vectorization service at one time, which does not conform to the batch array input format required by most vectorization services.
- Phenomenon: The retrieval result matching degree is insufficient, and the associated records of the target PACK or energy storage cabinet cannot be accurately located. Cause: The index model is not configured based on the multi-source heterogeneous data characteristics of this category, and the general model cannot adapt to the vector fusion requirements of data from different links.
- Phenomenon: When calling the API to obtain chunked index content in FastGPT v4.8.10, an empty array is returned. Cause: The original data query interface for chunked indexes is not available in this version. Corresponding content must be obtained by associating underlying storage through a custom script.
How to confirm the configuration is correct
- In FastGPT v4.17.0, upload a typical document of this category, such as an EOL test report, and confirm there are no truncation prompts during vector generation.
- Initiate a quality traceability retrieval, verify that the number of returned results matches the business expectation, and adjust the similarity threshold to a range that meets the requirements.
- Call the FastGPT API, verify that the chunked index content can be obtained normally, and confirm that the associated fields have been correctly configured.
- Simulate a high-frequency update operation, check that the incremental index is generated normally, and there is no system prompt triggered by full index reconstruction.
The values provided are common starting points and should be measured against the reader's own samples.