What Data for This Category Looks Like
Data for this category primarily comes from concentrate and auxiliary material analysis reports, production DCS systems, electrolyzer voltage and current monitoring data, laboratory test results, and energy and environmental ledgers. Daily documents include batching lists, smelting records, electrolyzer condition logs, test reports, anode and cathode quality records, environmental ledgers, and more. Most fields cover element content, temperature, current values, batch numbers, emission concentrations, and similar metrics. Units include percentage, degrees Celsius, amperes, tons, milligrams per cubic meter, and others. Data update frequency shifts in real time based on raw material batches, furnace heats, or electrolyzer conditions. The update interval for core production metrics is measured in minutes.
Constraints on Knowledge Base Retrieval and Recall
Data for this category is scattered and updated frequently. Retrieval systems must support fast cross-source associated recall to prevent audit delays from data lag. Documents contain many specialized industrial parameters and long paragraph records. Proper text splitting logic must be used to retain complete business logic and avoid losing valid information. Supplier and process quality audits require linking production data across multiple batches. Retrieval must support precise cross-collection recall. It is also necessary to balance the number of recall results and model context capacity to stop redundant data from interfering with audit judgments.
Configuration Settings
These configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Semantic similarity threshold for matching industrial specialized terminology, filters low-relevance non-audit data |
limit | Top 8–12 entries | Covers the number of core business parameters for a single batch of smelting or electrolysis, controls context length to fit model limits |
searchMode | mixedRecall | Balances precise matching of specialized terminology and associated recall of multi-source production data, adapts to the multi-field characteristics of this category's data |
usingReRank | Enabled | Performs secondary sorting on recall results, filters out historical electrolyzer condition or smelting records unrelated to the current audit batch |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands specialized terminology related to audits. For example, expands "electrolyzer voltage drop" to "cell voltage fluctuation" and "current stability" |
collectionIds | Bind corresponding collections by audit scenario | Only recalls data from specified collections such as current supplier qualifications, production ledgers, and environmental records, avoids interference from irrelevant data |
Three Common Configuration Mistakes
- Issue: Setting text block split length to 3000 characters results in lost batching list or smelting record blocks. Cause: Splitting is enforced by character length without accounting for industrial document paragraph boundaries, leading to valid business content being truncated and omitted.
- Issue: Rate limit errors occur during knowledge base vectorization. Cause: Parallel processing thread count is not adjusted. Simultaneous vectorization of multiple documents exceeds the interface rate threshold, triggering call restrictions.
- Issue: Audit queries take a long time to return results. Cause: The number of recall entries is set too high, or re-ranking is not enabled. Large volumes of irrelevant data participate in subsequent semantic processing, slowing the overall workflow.
How to Verify Correct Configuration
- Submit a query for a single-batch electrolysis audit, and confirm that recall results include core data such as the corresponding batch’s electrolyzer condition records and test reports.
- Review vectorization task logs to confirm there are no rate limit errors, and that thread count configuration matches current server resources.
- Test response speeds across different numbers of recall entries, and adjust configurations to meet business response time requirements.
- Test cross-collection retrieval functionality, and confirm that only data from bound audit-related collections is returned, with no irrelevant data included in the result set.
The values listed above are common starting points. They should be validated against the reader's own samples.