What Data Looks Like for This Category
Data for this category comes from registered or archived formulas, raw material safety documents, weighing and emulsification equipment, ERP/MES systems, laboratory and packaging material version management modules. Daily generated documents include formula process sheets, weighing and feeding records, emulsification or filling records, cleaning and site clearance records, semi-finished and finished product inspection reports, and label review forms. Data updates are triggered with each production batch or each line change. Each individual document contains fields such as formula number, raw material name, feeding amount, emulsification temperature, batch number, and archive identifier. Units include grams, degrees Celsius, and batch numbers, among others.
Constraints on Vector Models and Indexing
High-frequency updates triggered by each production batch or line change require indexes to support incremental synchronization, eliminating resource consumption from full index reconstruction. Structural differences across multiple document types require chunking strategies tailored to records of varying lengths, preventing critical business information from being split apart. Fields with identifiers and numerical values such as archive numbers and feeding amounts require vector models to accurately match structured information. The unified indexing requirement for multi-source data ensures that ledger formats exported from different systems can be automatically identified and processed, while also maintaining recall accuracy to avoid risks of inconsistent formulas and archived records in subsequent reports.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average length of a single production ledger record, avoiding splitting critical information across batches or documents |
| Overlap Length | 50–80 characters | Preserves coherent information such as raw material feeding order and process parameters across chunks |
| Similarity Threshold | 0.75–0.85 | Distinguishes production records of different batches, avoiding confusion between different batches with similar archive numbers |
defaultToken | 4096 | Adapts to long text splicing requirements during production ledger summarization |
maxToken | 8192 | Supports batch import of complete ledger data for multiple batches |
| Single Search Limit | Top 10 entries | Covers the number of associated records required for a single production report |
Three Common Configuration Errors
- A 404 status code appears in the interface, and vector model configuration cannot be saved. Cause: The network address for model calls is not configured correctly, or the selected model does not meet the compatibility requirements of FastGPT v4.17.0.
- The index construction task remains in the processing state indefinitely, eventually timing out and failing. Cause: The chunk length is set too large, causing the token count for single-batch processing to exceed the
maxTokenlimit. - After importing production records following a line change, search results do not include the new data. Cause: Incremental index update mode is not enabled, so newly uploaded ledgers are not synchronized to the existing index.
How to Verify Proper Configuration
- Upload a single formula process sheet, and confirm that the chunked text fully retains core fields such as formula number and raw material list.
- Submit a production ledger summary request, and check whether the number of returned associated records matches the expected range for the business scenario.
- Rebuild the index after modifying the vector model configuration, and confirm that no error prompts appear in the interface and the task completes normally.
- Compare search results for production records from different batches, and confirm that similarity matching results can distinguish differences between batches.
The values provided here are common starting points and should be evaluated against the reader's own samples.