Data Structure for This Category
This category’s data primarily originates from formula and process libraries, raw material batch ledgers, reactor DCS systems, weighing systems, LIMS laboratory information management systems, and warehouse management systems. Data updates trigger per production batch and each key process step. Daily documents include batch production instructions, material feeding verification slips, reaction process records, cleaning validation records, test reports, and deviation and change orders. Data fields include batch number, feeding amount (kilograms/liters), reaction temperature (degrees Celsius), reaction duration (hours), cleaning validation results, and additional fields. Some documents require cross-field information association across multiple systems.
Constraints for Vector Models and Indexes
The multi-batch, high-frequency update rhythm requires indexes to support fast, precise recall filtered by batch dimension, to prevent irrelevant cross-batch data from interfering with traceability results. The cross-system, multi-document association feature requires vector models to support cross-field semantic association, to accommodate association matching across documents from different sources. While single-batch data volume is small, updates occur frequently, so token consumption per single write must be controlled to avoid exceeding defaultToken or maxToken limits. Additionally, confidentiality requirements for formulas and processes mean index recall results must strictly restrict permission scope. When splitting text, context association of process steps must be preserved, to avoid breaking reaction logic via improper chunking.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the typical paragraph length of single process records for this category, to avoid splitting that breaks semantic coherence of reaction steps |
| Overlap Length | 100–200 characters | Retains process context across adjacent chunks, to prevent reaction logic across steps from being truncated by chunking |
| Paragraph Recognition Depth | 3 | Adapts to the paragraph hierarchy of process documents for this category, to accurately identify context association of reaction steps |
| Vector Dimension | 128 dimensions | Matches the output dimension of commonly used embedding models for this category, balancing index storage and recall accuracy |
| Similarity Threshold | Calibrated via actual testing | Must be adjusted based on semantic characteristics of deviation records and test reports for this category, to avoid recalling irrelevant batch data |
| Single Search Limit | Top 8 results | Adapts to the volume of associated data for single-batch issues in this category, balancing recall accuracy and response speed |
| Reranking Switch | Enabled | Optimizes semantic relevance of recall results for cross-field associated data across multiple systems |
| Maximum Context Length | 4000–6000 tokens | Accommodates multiple associated documents per single batch, meeting full-link information retrieval needs during quality traceability |
Three Common Configuration Errors
- A prompt reading “No available index model detected” appears after refreshing the page. This occurs when process and test documents for this category are not associated with the specified vector model, or the model configuration was not saved and activated.
- A format error is returned when using a multimodal embedding model. This occurs when the embedding format for non-text fields of this category’s documents (such as temperature curve data from DCS systems) is not adapted, causing the model to fail to properly parse input.
- Old vector database data cannot be recalled normally after upgrading to a new version. This occurs when chunking rules or vector dimensions do not match between old and new versions, and a batched data reindexing process was not completed.
How to Verify Proper Configuration
- Navigate to the knowledge base management page, check the status of the vector model associated with this category, and confirm the model is available and authorized.
- Upload a typical material feeding verification slip for this category, perform manual chunking testing, and confirm chunk length matches the preset configuration.
- Initiate a simulated quality traceability query, verify that the batch number and process steps in the recall results match the query conditions.
- Check the index write log, confirm that token consumption per single write does not exceed the
maxTokenlimit. - Confirm that FastGPT v4.17.0 is being used, to avoid version compatibility issues.
The values provided here are common starting points, and should be validated against samples relevant to the specific deployment.