What this category of data includes
This category of data originates from formula management systems, raw material batch ledgers, internal mixers, Mooney viscometers, vulcanizing machines, and laboratory testing equipment. Daily documents include formula sheets, weighing logs, mixing curves, rubber compound rapid test reports, vulcanization process cards, mold usage records, and finished product performance reports. Data updates align with each raw material batch, each batch of rubber compound, and each vulcanization operation per mold cycle. Document fields cover mixing temperature, vulcanization duration, rubber compound Mooney viscosity, finished product tensile strength, and other parameters, with standard industrial units such as ℃, minutes, MU, and MPa attached.
Constraints on vector models and indexes from these data characteristics
This category of data has high update frequency and covers multiple types of industrial documents. Indexes must support high-frequency incremental synchronization to prevent deviations in exception report drafting caused by delayed production data. Documents contain numeric fields with professional units and process description text. Vector models must adapt to rubber industry-specific terminology and unit systems to ensure accurate semantic encoding and avoid parameter matching errors. Single-record data volume is small, but total data volume grows with production pace. Index sharding must accommodate small-batch, high-frequency write scenarios to prevent write blocking. Additionally, exception reports are linked to multi-dimensional production data, so indexes must support cross-document associated recall to ensure accurate process exception traceability.
Configuration Settings
The following configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average length of single process documents in this category, avoids disrupting the integrity of professional terms and parameters during chunking |
| Overlap Length | 100–150 characters | Covers cross-chunk mixing and vulcanization connection logic, prevents loss of key production association information after chunking |
| Similarity Threshold | 0.75–0.85 | Meets semantic similarity requirements for industrial process parameters, filters low-correlation exception reports and process documents |
maxToken | 4096 token | Accommodates vector encoding length for batch process data in this category, avoids truncation of key parameters and description content |
| Single Search Limit | Top 8 entries | Balances recall efficiency and information completeness for exception traceability, covers multi-dimensional production association data |
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 Errors
- Phenomenon: Knowledge base indexes deployed on a PG database fail to build consistently, and the interface displays the index status as failed. Cause: No index sharding strategy adapted for small-batch high-frequency writes is configured, and the 8c16G host has no GPU resources, leading to vector model loading timeout.
- Phenomenon: Calls to external Embedding models throw errors, and vectors cannot be generated. Cause: No dedicated index model is configured separately; relying on generic models fails to adapt to semantic encoding of rubber industry professional terms.
- Phenomenon: Environment variable modifications do not take effect after starting via docker-compose, and index creation has no response. Cause: New configuration files are not loaded by recreating containers; directly modifying configurations does not trigger container updates.
How to Verify Proper Configuration
- Upload a typical document of this category (such as a vulcanization process card), and check whether the chunked content retains complete professional fields and unit information.
- Initiate a query based on abnormal process parameters, and verify whether the recall result association dimensions cover the full process data of mixing, vulcanization, and finished product testing.
- Check the deployment container’s running logs, and confirm that no memory or timeout errors occur during vector model loading and index writing.
- Upload new production batch data, and verify whether the index synchronization completion time matches the update rhythm of this category.
The values provided are common starting points and should be measured against the reader's own samples.