What the Data for This Category Looks Like
The core data for this category comes from the formula and process library, raw material batches, reactor DCS, weighing system, LIMS, and warehouse management system. The update frequency is once per production batch or each key process step. Daily documents include batch production instructions, feeding review slips, reaction process records, cleaning records, inspection reports, deviation and change orders. Fields include batch number, feeding amount (kg/L), reaction temperature (℃), cleaning time, deviation description, and others. Most single documents combine structured text and parameter values, and some documents contain confidential formula details.
Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall
Multi-batch, multi-step production modes require retrieval to accurately filter by fields such as batch number and process step, to avoid mixing documents across batches. Frequently updated documents need support for near-real-time recall to ensure retrieval results align with the current production batch. Coexistence of structured fields and confidential content requires retrieval to balance keyword matching and vector recall, while preventing formula information leakage. The number of documents related to a single production batch is limited, but their fields have strong correlations. Fragments highly relevant to the current question must be prioritized for recall, to avoid redundant results interfering with inspection and audit workflows.
Configuration Settings
The following configurations are based on FastGPT v4.17.0, and are adapted to the document characteristics and business requirements of this category.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.38–0.42 | Fine chemical documents have precise fields. A threshold that is too low will introduce irrelevant fragments, while a threshold that is too high will fail to match structured fields such as batch numbers |
limit | 8 recall upper limit | The number of documents related to a single production batch is limited. Too many recalls will increase context redundancy, while too few will fail to cover complete process steps |
usingReRank | Enabled | Batch synthesis process steps have strong correlation. Reranking can prioritize fragments with higher step order and parameter matching relevance |
searchMode | mixedRecall | Balance structured keyword matching for fields such as batch number and feeding amount, and vector matching for process descriptions, to improve accuracy |
datasetSearchUsingExtensionQuery | Enabled | Expand question synonym matching to cover different expressions of process terms such as feeding and reaction endpoint |
collectionIds | Bind the knowledge base collection corresponding to the category | Isolate documents of different products to avoid mixing cleaning records and other batch documents that cause cross-contamination |
Three Common Misconfigurations
- Issue: The reranking model fails to work during large model calls, but functions normally during separate knowledge base testing. Cause: The
usingReRankconfiguration is not enabled in the large model call workflow, and reranking is only enabled separately during the knowledge base retrieval test. - Issue: Corpus retrieved only via the API cannot display answer sources. Cause: Retrieved knowledge base fragments are not bound to the large model response, and source association logic is not configured.
- Issue: Knowledge base recall results contain duplicate fragments and fail to accurately match target feeding records. Cause: Dataset deduplication configuration is not enabled, the
similaritythreshold is set too high, and keyword matching mode is not combined.
How to Verify Successful Configuration
- Navigate to the FastGPT knowledge base management page, and check whether the
usingReRankanddatasetSearchUsingExtensionQueryconfigurations for the corresponding collection are enabled. - Initiate a knowledge base retrieval test, enter a question containing a specific batch number, and confirm whether the recall results include the feeding review slip or reaction process record for that batch.
- Call the large model API, pass in a question with a batch number and process keywords, and check whether the returned result includes source information for the knowledge base fragments.
- View the retrieval log, confirm that the
similarityvalue of the recall results falls within the preset 0.38–0.42 range, and that no duplicate fragments are present.
The values provided here are common starting points, and should be evaluated against the reader's own samples.