What the data for this category looks like
Core data for this category comes from formula systems, raw material batch management, internal mixers, Mooney viscometers and rheometers, vulcanizers, and laboratory testing links. Daily documents include formula sheets, weighing records, mixing curves, rubber quick test reports, vulcanization process cards, mold usage records, and finished product performance reports. Data updates occur per raw material batch, per batch of feed material, and per mold production run. Individual documents contain fields such as raw material ratio quantities, feeding time, temperature curve nodes, vulcanization duration and temperature, with units including degrees Celsius, minutes, batch numbers, weight units, and more.
What constraints these characteristics impose on the vector model and index stage
High-frequency updates per batch and per mold run require indexes to support incremental synchronization, avoiding resource consumption and time delays from full reindexing. The coexistence of multiple document types requires chunking strategies adapted to the length characteristics of different documents. For example, sequential text from mixing curves and structured fields from formula sheets need unified chunking logic to prevent key information from being split apart. Strong associations exist between data points: for example, weighing records and mixing curves from the same batch must maintain association in vector space, so indexes must retain batch-related metadata between documents. Additionally, inspection and audit links have high confidence requirements for recall results, so vector retrieval matching accuracy must meet process judgment standards to avoid misjudgment or missed judgment.
How to set configurations
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of single documents in this category, covers single-segment sequential data of mixing curves and complete field groups of formula sheets, avoids vector fragmentation caused by overly short chunks |
| Overlap Length | 100–150 characters | Retains associated information between adjacent batches or different documents of the same batch, avoids loss of associations caused by chunk breaks |
defaultToken | 2048 | Matches the token usage of most documents in this category, adapts to input limits of general embedding models |
maxToken | 4096 | Covers token requirements for long documents such as complete mixing curves, avoids truncation of key process data |
| Similarity Threshold | 0.75–0.85 | Distinguishes matching accuracy for qualified and unqualified process parameters, adapts to judgment requirements for inspection and audit |
| Single Search Limit | Top 6 entries | Matches the number of associated documents that need to be referenced during inspection in this category, avoids redundant recall interfering with judgment |
Three Common Misconfigurations
- Embedding model calls return 503 errors. The cause is incorrect model group configuration, with the default group not bound to an available text-embedding model.
- Index tasks in Docker-deployed FastGPT remain in a running state with no progress. The cause is that chunk length is set too large, leading to timeout when processing single-batch documents, which does not adapt to the high-frequency update rhythm of this category.
- Chat models can be called normally but indexes make no progress. The cause is that a dedicated embedding model is not separately configured for the index link, and parameters from the chat model are misused, leading to vector generation failure.
How to confirm proper configuration
- View vector index task logs in FastGPT v4.17.0, confirm that documents for each batch have successfully generated vectors and been written to the index, with no error messages.
- Test retrieval for formula sheets and mixing curves from a specific batch, confirm that recall results include associated documents that match business logic.
- Verify that the return status code for embedding model calls is 200, with no timeout or group error prompts. Adjust the specific value of the similarity threshold according to business requirements.
- Compare index time consumption for incremental updates and full updates, confirm adaptation to the update frequency of per raw material batch, per batch of feed material, per mold production run.
The values given are common starting points and should be measured against the reader's own samples.