What the Data for This Category Looks Like
Data for this category mainly comes from concentrate and auxiliary material analysis, DCS systems, cell voltage and current monitoring, laboratories, and energy and environmental monitoring equipment. Daily documents include batching lists, smelting records, electrolytic cell condition records, laboratory reports, anode and cathode quality records, and environmental protection ledgers. Common fields include cell number, batch number, impurity content, current and voltage values, and other metrics. Units include millivolts, percentages, tons, and others. Data updates run per batch of raw materials, per furnace or per cell, with real-time updates. The size of individual documents varies based on the recording cycle.
What Constraints Do These Characteristics Impose on the Knowledge Base Retrieval and Recall Link?
Real-time electrolytic cell condition and smelting record data require the retrieval pipeline to support low-latency recall. This prevents data lag from affecting equipment inspection judgments. Multi-source heterogeneous document formats and differentiated fields including cell number and impurity content require retrieval to support keyword matching and field mapping across different document structures. This ensures accurate association with business dimensions. High-frequency incremental batch data creates knowledge base synchronization pressure, requiring adaptation to batch incremental import capabilities. Complex raw material composition balance and cell group management content require retrieval and recall to accurately match multi-dimensional business fields. This avoids generalized results interfering with maintenance decisions.
How to Configure the Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Non-ferrous metal smelting data contains many technical terms. Filter low-correlation results to ensure retrieval results match business scenarios |
limit | Top 8–12 entries | Inspection of electrolytic cell groups and smelting furnace groups requires coverage of multi-device associated data. Too many results increase decision-making burden, too few results miss critical information |
usingReRank | Enabled | Semantic similarity of professional documents needs to be optimized through reranking, to improve the ranking priority of maintenance-related content |
datasetSearchUsingExtensionQuery | Enabled | Smelting industry terms have multiple expression forms. Use extended queries to cover synonymous expressions such as "cell voltage" and "electrolytic cell current" |
searchMode | mixedRecall | Balance semantic matching of professional terms and precise retrieval of structured fields, adapting to retrieval needs of multi-source heterogeneous documents |
collectionIds | Bind corresponding knowledge base collections by business department | Store documents from smelting, electrolysis, and laboratory departments in classified storage, to avoid interference from cross-department irrelevant data in retrieval |
Three Common Configuration Mistakes
- Phenomenon: The number of results returned by a single retrieval is far lower than the configured
limitvalue, or the interface prompts that the reference upper limit is exceeded. Cause: Independent knowledge base collections are not divided by business departments such as smelting, electrolysis, and laboratory. Cross-collection retrieval triggers the global reference upper limit restriction. - Phenomenon: When batch importing 50000+ MD documents, the API call triggers a maxLength error. Cause: The total document volume and total character count of a single batch import are not split. Single-batch processed data exceeds the platform's single-request length threshold.
- Phenomenon: Retrieval response time exceeds 10 seconds, or returned results do not match key business fields such as cell number and impurity content, or custom tags do not take effect. Cause:
usingReRankis not enabled, or thelimitvalue is not restricted, and field mapping rules are not configured, leading to retrieval pipeline delay or failure to accurately associate business parameters.
How to Verify That the Configuration Is Correct
- Initiate a test query containing technical terms, check whether the returned result fields include business-related content such as cell number and impurity content, and adjust the
similaritythreshold to a range that meets business needs. - Batch import a small number of test documents, check the import progress and error logs, adjust the number of documents and total character count per batch import, to avoid triggering the maxLength restriction.
- View the response time of the retrieval pipeline. Adjust the
limitvalue and the enabled status ofusingReRankbased on business real-time requirements, to balance retrieval efficiency and result relevance. - Verify the binding effect of custom tags. Initiate a query with tag keywords, confirm whether the returned results only include documents of the corresponding category.
The values provided are common starting points and should be measured against the reader's own samples.