What data for this category looks like
Data sources include raw material batch ledgers, production MES systems, slurry and coating testing equipment, workshop dew point monitoring systems, formation and grading cabinets, and laboratory safety test reports. Daily documents include batching records, coating and rolling parameter tables, winding or lamination process records, liquid injection operation logs, formation and grading data, OCV and internal resistance test reports. The system generates batching records per slurry batch, uploads coating test data per electrode roll, and syncs formation and grading data after each cell is completed. Some parameters such as workshop dew point update in real time. Document fields include professionally labeled content with units, such as solid content, areal density, rolling thickness, dew point temperature, and formation curve parameters.
What constraints these characteristics impose on source citation and traceability
The system binds unique identifiers such as batch numbers and cell IDs for traceability, avoiding cross-batch reference confusion. This is required because data updates follow per-batch, per-cell, or real-time schedules. Professional parameters use dedicated units. The system retains full units during retrieval and citation to ensure accuracy of process information. The system marks multi-source data with clear affiliated system labels. This prevents parameter mixing across different production processes. The system configures real-time synchronization for frequently updated data. This stops referencing expired production records. The system controls the length of referenced content to avoid exceeding model context limits. This addresses wide variation in individual process document lengths.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
quoteMaxToken | 800–1200 token | Matches the average length of single process documents for lithium-ion cell manufacturing, avoids truncating critical parameters |
detail | Enabled | Used to return full fields of referenced documents, such as professional parameters including coating areal density and rolling thickness |
| Single search limit | Top 3 entries | Adapts to the scale of single-batch process data, avoids context overflow caused by excessive citations |
| Citation display condition | Show only when valid matches are retrieved | Prevents displaying invalid citations when the local knowledge base has no matching content |
| Maximum context length | 4000–6000 characters | Adapts to summary of multi-source process data, aligns with context window requirements of general large models |
Three common misconfigurations
- Setting
quoteMaxTokentoo small truncates critical parameters such as coating areal density and formation curves. This results in incomplete referenced content. The issue stems from failure to account for the actual length of lithium-ion cell process documents. - Failing to enable the
detailparameter returns only scattered text fragments for references. Professional process parameters with units cannot be displayed, failing to meet process verification needs. The issue stems from failure to enable full field return configuration. - Failing to set citation display conditions forces citation entries to appear even when the local knowledge base has no matching batch process data. This creates on-screen prompts for citations with no actual content. The issue stems from failure to configure empty match filtering rules.
How to confirm proper configuration
- Queries targeting coating and rolling parameters for specific batches are submitted. Returned references are checked for complete parameter fields and source system identifiers.
- Scenarios with no matching content in the local knowledge base are simulated. Citation entries are confirmed to not be forcibly displayed.
- The
quoteMaxTokenvalue is adjusted. Returned referenced content length is verified to align with the set range. - The FastGPT system configuration interface is accessed. The current running version is confirmed to be FastGPT v4.17.0.
The values provided are common starting points and should be measured against samples matching the specific deployment context.