What data from this category looks like
Typical enterprises in basic chemical continuous production include those producing chlor-alkali, synthetic ammonia, methanol, soda ash, and similar products. Daily documents cover operating procedures, process indicator cards, DCS trends, laboratory reports, interlock bypass records, special work permits, and shift logs. Data sources include DCS/SIS systems, online analyzers, LIMS systems, equipment condition monitoring tools, energy metering devices, and raw material storage tanks. Data update frequencies fall into three categories: real-time, per shift, and daily. DCS trends update in real time. Shift logs update per shift. Laboratory reports update daily. Document fields include timestamps, process parameter values, equipment numbers, approvers, and other fields. Some parameters use dedicated units such as ℃, MPa, t/h.
What constraints these characteristics impose on knowledge base retrieval and recall
Multiple heterogeneous data sources require retrieval logic to adapt to structured process parameter data, semi-structured shift logs and work permits, unstructured operating procedures, and other document formats. Different update frequencies require the knowledge base synchronization frequency to match business nodes. Real-time data must be pulled for updates at set intervals. Shift logs and laboratory reports must be synchronized on a per-shift or daily cycle. Specific parameter units require matching field units during retrieval, to avoid recall bias caused by unit mismatches. Unlike the multi-component fractionation scenario in petroleum refining, strong interlock control in basic chemical continuous production requires retrieval to prioritize interlock-related documents. This reduces misoperation risks.
Configuration Settings
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches the precise matching requirements of basic chemical process parameters, prevents low-relevance non-core documents from appearing in recall results |
limit | Top 6–10 entries | Covers key information from multiple core documents including shift logs, DCS trends, laboratory reports, and controls the total context length |
searchMode | mixedRecall | Adapts to different retrieval logic for structured process data and unstructured operating procedures, improves recall coverage |
usingReRank | Enabled | Rearranges recall results by business relevance, prioritizes returning core safety and production documents such as interlocks and process indicators |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands synonyms for process-related terms, adapts to the colloquial terminology habits of on-site operators |
collectionIds | Calibrated through actual testing | Binds dedicated knowledge base collections for categories such as chlor-alkali and synthetic ammonia, prevents irrelevant documents from other business scenarios from being included |
Three Common Configuration Errors
- Symptom: Calling the FastGPT chat API returns the error "No knowledge base selected", or recall results are unrelated to the current production scenario. Cause: The
collectionIdsconfiguration item is not correctly passed in the request parameters, or the passed collection ID does not match the actual ID of the target knowledge base. - Symptom: Importing documents from internal web links returns a 401 unauthorized error, and the import cannot be completed. Cause: No web crawling login credentials or proxy is configured for the knowledge base, so documents requiring internal login cannot be accessed.
- Symptom: Recall results work normally during debug preview, but the online chat scenario prompts "No knowledge base selected". Cause: The online deployed FastGPT instance is not correctly associated with the specified knowledge base collection, or the access permissions for the knowledge base are not configured in the environment variables.
How to Confirm the Configuration Is Complete
- Check if the
collectionIdsbound to the knowledge base match the document collection of the current business scenario. Verify that the collection names match the actual stored document types such as operating procedures and DCS trends. - Initiate a test query, enter a question related to process parameters. Verify that the recall results include corresponding types of documents, and confirm that the
similaritythreshold meets the business accuracy requirements. - View the order of recall results. Confirm that after
usingReRankis enabled, core documents such as interlock bypass records and process indicator cards appear at the top of the results. - Call the API interface with correct parameters. Verify that the returned results have no missing parameters or unauthorized errors, and that the recalled content matches business requirements.
The values provided here are common starting points, and should be evaluated against the reader's own samples.