What this category's data looks like
Core data for this category comes from concentrate and auxiliary material analysis, DCS systems, cell voltage and current monitoring, laboratory testing, and energy and environmental monitoring equipment. Daily documents include batching lists, smelting heat records, electrolytic cell condition logs, laboratory reports, anode and cathode quality records, and environmental protection ledgers. Fields covered include grade, current intensity, cell number, impurity content, recovery rate, and more. Units include percentage, kiloampere, and cubic meter. The frequency of data updates adjusts with production batches; some key parameters are updated in real time.
Constraints on Knowledge Base Retrieval and Recall
Multi-source real-time updated data requires the retrieval link to have low latency, to support real-time traceability of furnace or electrolytic cell conditions. Diverse document types and highly specialized fields require precise matching of professional terminology and business scenarios. Generalized retrieval can easily introduce irrelevant data. A single production batch is associated with multiple documents, such as smelting heat records, cell condition data, laboratory reports, and batching lists for the same heat. Retrieval must cover the associated document set while avoiding redundant results. High data update frequency requires that retrieval can promptly recall newly uploaded production records to adapt to real-time analysis needs.
Configuration Settings
This configuration is compatible with FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
similarity | 0.75–0.85 | This category requires precise matching of cell condition and laboratory data. A threshold that is too low will introduce irrelevant impurity analysis records, while a threshold that is too high will miss associated historical data |
limit | Top 8–12 results | This category associates multiple documents with a single production batch. Too many results will exceed the context window, while too few will fail to cover the complete traceability chain |
searchMode | mixedRecall | This category requires precise matching of professional terms such as cell voltage and anode slime, and association of ingredient data from batching lists. The hybrid mode balances semantic and keyword recall |
usingReRank | Enabled | This category has strong correlations between data fields, and reranking can filter historical records that are semantically similar but irrelevant to the current query, improving traceability accuracy |
datasetSearchUsingExtensionQuery | Enabled | This category requires cross-document information association of raw materials, furnace conditions, and laboratory data. Extended queries can supplement implicit association conditions |
collectionIds | Configured by document type grouping | Documents in this category are divided into smelting, cell condition, laboratory, and other categories. Grouped retrieval reduces the volume of irrelevant recalled data |
Three Common Misconfigurations
- Symptom: Knowledge base search throws error
invalid configuration parameter name "hnsw.max_scan_tuple". Cause: Incorrectly pasting parameter names from other tools into FastGPT configuration items, without using the officially supported parameter list. - Symptom: After batch uploading 100 5KB production documents, approximately half of the files fail to train, and duplicate references appear when re-uploading. Cause: Failure to verify the matching between file batch identifiers and historical records, and failure to enable the automatic deduplication logic based on file hashes.
- Symptom: Setting the
limitparameter to more than the top 20 results leads to redundant retrieval results, making it impossible to quickly locate abnormal cell conditions, and retrieval latency exceeds 5 seconds. Cause: Assuming that a larger reference upper limit means more comprehensive recall, without considering the actual number of associated documents per production batch in this category, and without enabling the reranking function to filter redundant results.
How to Verify Successful Configuration
- Initiate a search for an electrolytic cell with abnormal current, and check whether the returned results include the historical cell condition records of that electrolytic cell, as well as the batching list and laboratory report for the corresponding smelting heat.
- View the retrieval log to confirm that the number of returned documents matches the configured range of the
limitparameter, and that the similarity scores fall within the range set by thesimilarityparameter. - Upload a newly added electrolytic cell condition record, initiate an associated search, and confirm that the system can recall the content of the new document to verify adaptation to real-time updates.
- Test retrieval results before and after enabling
datasetSearchUsingExtensionQuery, and confirm that the extended query can recall cross-document associated data, such as the association between raw material ingredients and cell condition abnormalities.
The values provided are common starting points and should be measured against the reader's own samples.