What the data for this category looks like
Covers smelting, electrolysis, laboratory, and scheduling departments at copper, aluminum, lead-zinc, and other smelting enterprises. Daily documents include batching lists, smelting records, electrolytic cell status records, laboratory reports, anode and cathode quality records, and environmental protection ledgers. Data sources include concentrate and auxiliary material analysis, DCS systems, cell voltage and current monitoring systems, laboratory testing, and energy and environmental monitoring equipment. Update frequency adjusts in real time alongside material batches, furnace heats, or electrolytic cell operations. Core indicators including cell voltage and current update once per second. Batching lists and laboratory reports generate per individual batch. Document fields use professional units, such as cell voltage (mV), current strength (A), metal grade (%), impurity content (ppm), and similar units.
Constraints imposed by these characteristics on vector models and indexing workflows
High-frequency real-time updates require indexes to support incremental construction and rapid refreshing, to avoid excessive resource consumption from full reconstruction. Multi-field document structures with professional units require retaining field context during chunking, to prevent truncation of critical information that causes vector representation deviations. Cross-departmental document association needs require indexes to support cross-type document recall, such as matching batching lists to electrolytic cell status records. Unlike the iron-carbon system used in steel smelting, this category focuses on association data between impurity components and electrolytic cell groups. Vector models must adapt to professional terminology used in industrial smelting scenarios, to improve recognition accuracy for statements such as impurity content and metal recovery rate.
Configuration Settings
| Configuration Item | Recommended Range | Rationale for This Setting |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the single-paragraph field length of smelting documents, avoids truncating critical content including batching ratios and cell status parameters |
| Overlap Length | 100–150 characters | Retains contextual connections between adjacent chunks, prevents professional terminology from being split across chunks |
| Similarity Threshold | 0.72–0.85 | Calibrated via on-site testing, filters low-relevance non-professional documents and retains recall results strongly tied to smelting processes |
| Single Search Limit | Top 8–12 results | Balances recall coverage and subsequent inference context length, adapts to process queries that require association across multiple documents |
| Maximum Context | 4000–6000 token | Complies with maxToken parameter settings, accommodates vector content and query text from multiple smelting documents |
| Default Token Count | 2000–3000 token | Adapts to the defaultToken parameter, fills context for basic recall and controls single-call costs |
Three Common Misconfigurations
- Symptom: Embedding model fails to connect to OneAPI, with error prompts indicating interface authentication failure or request timeout. Cause: Input format of the vector model has not been adjusted to fit the field structure of smelting professional documents, or OneAPI endpoint mapping rules have not been configured correctly.
- Symptom: After enabling the Rerank model, no effective sorting changes appear in online recall tests. Cause: Input for the rerank model has not been adjusted to include complete document fragments with professional fields, or the similarity threshold is set outside the applicable range, preventing the rerank model from triggering valid calculations.
- Symptom: Some laboratory data vector representations are missing after index construction completes. Cause: Chunk length is set too short, truncating document fragments containing critical fields such as impurity content and metal grade, leading to incomplete vectorization of corresponding content.
How to Verify Correct Configuration
- Review the index construction logs in FastGPT v4.17.0, confirm that configuration items such as chunk length and overlap length match the set values.
- Input a process-related query, such as "Abnormal voltage causes for a specific batch of aluminum ingot electrolytic cells", verify that recall results include target documents such as electrolytic cell status records and smelting records.
- After enabling the rerank function, check that the sorting of recall results matches professional relevance, confirming the rerank model is active.
- Check the vector model call logs, confirm there are no authentication errors or timeout errors, ensuring the model call link operates normally.
The values listed here are common starting points, and should be evaluated against samples used in actual operational scenarios.