What this category of data looks like
This category covers smelting, electrolysis, laboratory, and scheduling departments of copper, aluminum, lead-zinc, and other smelting enterprises. Daily documents include batching lists, smelting records, electrolytic cell condition records, laboratory reports, anode and cathode quality records, and environmental protection ledgers. Data sources include concentrate and auxiliary material analysis results, DCS system data, cell voltage and current monitoring data, laboratory test reports, and energy and environmental monitoring data. Update frequency is adjusted in real time based on raw material batches, furnace heats, or electrolytic cell conditions. Some ledgers are archived on a fixed cycle.
What constraints do these characteristics impose on the vector model and index link
The multi-source, heterogeneous data characteristics of this category require indexes to support chunking and associated storage of mixed-format content. Real-time updated condition data needs to adapt to incremental indexing mechanisms to avoid the time cost of full reconstruction. Field differences across documents are significant: for example, cell condition records include numerical fields with units such as cell voltage and current, while batching lists mark raw material component proportions. Chunking must retain field and context associations to avoid breaking business logic. Coexisting high-frequency updated real-time data and low-frequency archived ledgers require configuring a layered indexing strategy to balance retrieval efficiency and storage costs. Additionally, the business requirements of equipment inspection and maintenance Q&A require retrieval results to accurately match core condition information such as furnace conditions and cell conditions, which places high demands on recall accuracy.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Covers core business fields of a single electrolytic cell condition record or batching list, avoids breaking context associations through splitting |
| Overlap Length | 100–200 characters | Retains business connection information between adjacent chunks, prevents loss of associated logic during cross-chunk retrieval |
maxToken | Set to the selected embedding model/service maximum supported input tokens | maxToken is the input-token maximum supported by the selected embedding model/service; verify it in that provider configuration. See the v4.17.2 model-field reference. It is not retrieval/LLM context or a FastGPT-wide fixed cap. |
defaultToken | 200 token | Retains core parameters of a single equipment inspection record, avoids truncation of critical business information |
| Similarity Threshold | 0.72–0.85 | Filters low-relevance non-condition documents, accurately recalls content related to furnace or cell conditions |
| Single Recall Limit | Top 8 entries | Matches the daily retrieval needs of scheduling and inspection personnel, avoids excessive redundant results interfering with judgment |
Three common mistakes
- Symptom: After configuring baidu-embedding-v1, a 404 error is returned, and index model binding cannot be completed. Cause: The interface route and key permissions are not configured correctly, causing the embedding model call chain to interrupt.
- Symptom: After deploying the m3e model without a GPU environment, it cannot be added as an index model in the FastGPT interface. Cause: The CPU mode for running the embedding model container is not enabled, or CPU-adapted model running parameters are not specified in the configuration.
- Symptom: Irrelevant cell condition data across furnace heats appears in retrieval results, and the number of recalled entries does not meet expectations. Cause: The chunk length is set too small, splitting complete condition records for a single furnace heat, leading to loss of context associations.
How to confirm the configuration is correct
- Upload a single electrolytic cell condition record, and check the chunked text via the FastGPT v4.17.0 interface to confirm that core fields such as cell number, voltage, and current are retained, with no truncation or incorrect splitting.
- Initiate a retrieval for "Check the condition of electrolytic cell 1 today", and verify that the similarity scores of the recalled results fall within the configured threshold range.
- Import a batch of real-time DCS system data, and check whether the index automatically completes incremental updates with no prompt for full reconstruction.
- Test calling the embedding model interface, and confirm that the returned vector dimensions match the embedding model dimensions configured in FastGPT.
The values provided here are common starting points and should be measured against the reader's own samples.