What data for this category looks like
Data sources include refinery and chemical plant process logs, safety and compliance case files, technical renovation project application documents, environmental assessment acceptance documents, quality inspection and energy consumption reports. Batch updates run during a single project application cycle. Daily archiving syncs data on a quarterly basis. Most documents are multi-page PDFs or structured forms. They include fields such as process parameters, equipment numbers, approval nodes, and compliance indicators. Field units include industrial units like MPa, ℃, t/h, and cubic meters per hour. Individual document lengths range from hundreds of words to tens of thousands of words.
What constraints do these characteristics impose on knowledge base retrieval and recall
Retrieval must match both parameter values and their corresponding units for documents with multiple fields and industrial units. This prevents confusion between process data from different operating conditions. Chunking must retain adjacent node context for a high share of long documents. This preserves complete process logic. Field standardization mapping must complete before retrieval for heterogeneous data sources with large structural differences. This ensures consistent recall results. Recall scope must limit to the exclusive dataset for the current project for single project applications involving multiple document types. This avoids interference from cross-project data.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.85 | Matches the precision requirement for refinery and chemical industry terminology and process parameters. Avoids recalling irrelevant general compliance documents |
limit | Top 12 entries | A single application document involves multiple process nodes. Need to recall enough relevant case files while controlling context length |
searchMode | semantic | Addresses semantic matching needs for long texts and professional terms. Replaces generalized recall from keyword matching |
usingReRank | Enabled | Reorders recall results by semantic relevance to the query. Prioritizes results with high process parameter matching accuracy |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands professional term queries. For example, expands "atmospheric tower" to "atmospheric distillation tower" and "atmospheric unit". Covers synonyms in the refinery and chemical industry |
collectionIds | Fill in the IDs of bound refinery compliance case files and process log datasets as appropriate | Ensures only exclusive datasets associated with the current project are retrieved. Avoids interference from cross-project data |
Three common mistakes
- Phenomenon: Knowledge base retrieval throws the error
text index required for $text queryafter starting FastGPT v4.17.0. Cause: Full-text index configuration was not enabled when creating the dataset, or vector indexes were not rebuilt after upgrading the version. - Phenomenon: Retrieval results include unauthorized cross-project case files after deployment. Cause: The
collectionIdsparameter was not configured correctly. This leads to binding unrelated datasets. - Phenomenon: Retrieval results are empty after migrating the knowledge base. Cause: Vector index files for the dataset were not properly exported and imported. Only metadata was migrated, making query matching impossible.
How to confirm the configuration is correct
- Submit a query that includes specific process parameters and units. Check if returned result fields include matching equipment numbers and corresponding units.
- View retrieval logs. Confirm the number of recall results falls within the range set by the
limitconfiguration. - Test the difference in result sorting before and after enabling
usingReRank. Confirm the reranking function works properly. - Check if the bound
collectionIdsonly include compliance case files and process log data for the current project.
The values provided are common starting points and should be measured against the reader's own 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.