What this category of data looks like
The policy and regulatory reference data for refinery and chemical production draws on industry compliance policies and work safety standards released by national and local energy regulatory and ecological environment departments, plus internal production management systems, environmental emission rules, and equipment operation and maintenance specifications from the group. National-level policies update every three to six months. Internal documents are revised quarterly to meet compliance requirements. Individual documents vary widely in length, and include fields such as document number, issuing authority, effective date, clause content, and applicable scenarios. They use professional units including ppm, cubic meters, tons, and hours.
What constraints these characteristics impose on vector models and indexes
Documents include clear metadata fields. Indexing must associate and store document numbers, effective dates and other information to enable precise recall based on compliance time ranges later. Individual documents vary widely in length, from compliance notices of a few hundred characters to thousands of characters of regulatory compilations. A flexible chunking strategy is required to avoid information fragmentation. A large number of industrial technical terms and specific units are involved. A vector model adapted to industrial domain semantic encoding must be selected to ensure matching accuracy of professional content. Update frequencies are irregular and incremental updates are needed. Support for incremental embedding tasks must be configured to avoid resource waste from full index reconstruction.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the length span of refinery policy documents, avoids overly fragmented chunks that lose context, or overly long chunks that cause semantic encoding deviation |
| Overlap Length | 100–200 characters | Retains association of policy clauses across chunks, ensures semantic coherence of professional content |
| Similarity Threshold | 0.75–0.85 | Filters low-relevance recall results, adapts to semantic matching accuracy of industrial technical terms |
| Single Search Limit | Top 10 results | Controls the number of recall results, avoids exceeding the context carrying limit |
defaultToken | 8192 | Adapts to the embedding length of conventional policy documents, ensures core clauses are not truncated |
maxToken | 16384 | Covers the embedding needs of ultra-long compliance compilations, avoids loss of key content |
Three common configuration mistakes
- Switching vector models causes knowledge base embedding progress to stall and prevents rollback. The cause is failing to clear the original embedding cache first; directly modifying the model configuration creates index task conflicts.
- Testing returns a 404 error after configuring the embedding model. The cause is failing to correctly fill in the API key and service endpoint parameters for model access, so requests cannot match valid interfaces.
- Multilingual recall rate does not improve after batch re-embedding. The cause is failing to adjust chunking and semantic encoding configurations for multilingual documents, leading to failed cross-language semantic matching of technical terms.
How to confirm configuration is complete
- Access the knowledge base settings page in FastGPT v4.17.0, check the vector model configuration items, and confirm the selected model matches the current configuration.
- Upload a test refinery policy document, trigger the embedding task, and confirm the task status shows completed with no error logs.
- Initiate a test query related to refinery compliance, and confirm the similarity scores of the recall results fall within the preset threshold range.
- Attempt to modify the vector model configuration, confirm it can be saved and switched normally, with no progress freezes or inability to roll back.
The values provided are common starting points and should be measured against in-house test samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.