This document applies to FastGPT v4.17.0.
What Data Looks Like for This Category
Data for this category comes from formula systems, raw material batch records, internal mixers, Mooney and rheometer devices, vulcanizers, and laboratory testing equipment. Daily documents include formula sheets, weighing records, mixing curves, rubber quick test reports, vulcanization process cards, mold ledgers, and finished product performance reports.
Data updates sync with each shipment of raw materials, each batch of rubber compound, and each mold production run. Fields include rubber compound ratio values, weighing deviation ranges, mixing temperature ranges, vulcanization duration parameters, finished product hardness indicators, and more. Units include kilograms, degrees Celsius, minutes, and Shore hardness units.
What Constraints Do These Characteristics Impose on Vector Models and Indexes
This category has high data update frequency, small per-record data size, and a large number of batches. Adapt vector models and indexes to high-frequency write requirements.
Multi-source heterogeneous data has significant format differences. Use unified field mapping rules to avoid index confusion.
Key parameters of formula and process documents, such as mixing temperature and vulcanization duration, serve as core retrieval fields. Prioritize associating high-weight fields in indexes.
A single retrieval must cover multi-batch historical data. Align index recall ranges with the association of historical records across the entire production process.
Control index loading speed to meet the real-time requirements of bid quotation preparation.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the text length of single mixing curves and vulcanization process cards, avoids splitting key parameters within a chunk |
| Overlap Length | 50–80 characters | Retains cross-chunk process parameter associations, prevents loss of context for mixing temperature and vulcanization duration during retrieval |
maxToken | Set to the selected embedding model/service maximum supported input tokens | maxToken is the input-token maximum supported by the selected embedding model/service; verify it in that provider configuration. See the v4.17.2 model-field reference. It is not retrieval/LLM context or a FastGPT-wide fixed cap. |
| Similarity Threshold | 0.75–0.85 | Filters low-relevance historical data, focuses on compliant process parameters matching bid requirements |
| Single Search Limit | Top 10 entries | Controls the volume of retrieval results, adapts to response speed requirements for rapid bid document preparation |
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 Configuration Mistakes
- Retrieval results include non-corresponding batch mixing curves. Fail to configure index association rules based on raw material batch fields. Cross-batch data confusion follows, which may cause incorrect matching of process parameters in bid documents.
- Retrieval requests return timeout errors. Set an excessively high single search limit. Fail to limit the historical data range for index scanning. Exceed system response thresholds, which affects the preparation efficiency of bid quotations.
- Retrieval results for formula sheets have empty fields. Fail to include core fields of weighing records and rubber quick test data in index mapping. Lose key retrieval information during vector embedding, making it impossible to match the process compliance requirements required for bids.
How to Verify Correct Configuration
- Upload a single mixing curve document. Check that the chunk split after vector embedding retains complete temperature and duration parameters, with no critical content truncated.
- Enter a search term containing specific vulcanization process parameters. Verify that the number of recalled retrieval results matches the single search limit setting.
- Simulate uploading batch formula sheets. Observe whether the index update time matches the update frequency requirements of production data.
- Check the
maxTokenanddefaultTokenparameters on the configuration page. Confirm they match the input limit of the currently used vector model.
The values provided are common starting points and should be measured against the reader's own samples.