What the Data for This Category Looks Like
Daily documents for this category include batching lists, smelting records, electrolyzer condition logs, laboratory reports, anode and cathode quality records, and environmental protection ledgers. Data sources cover concentrate and auxiliary material analysis reports, DCS production systems, tank voltage and current monitoring data, laboratory test results, and energy and environmental protection monitoring equipment. Data updates occur in real time with each batch of raw material input, each furnace smelting run, or each electrolyzer operation. Individual documents contain fields such as raw material component proportions, furnace condition parameters, tank voltage and current, metal recovery rates, and pollutant emission indicators. Some fields must match the production process number of the corresponding batch.
Constraints Imposed by These Characteristics on Vector Models and Indexing
Real-time updated multi-source data requires indexes to support high-frequency incremental writes, and avoid performance losses from full index rebuilding. The multi-field mixed document structure requires vector models to adapt to semantic associations across multimodal fields, while supporting filtered retrieval by structured fields such as production batch number and tank number. Individual documents have high field complexity, so key production identifiers must be retained during chunking to avoid semantic fragmentation. Tender quotations must be linked to historical batch data, so indexes must support precise recall by time range and raw material type, while balancing recall speed and recall accuracy.
Configuration Settings
These configurations are adapted for FastGPT v4.17.0, as detailed below:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the typical paragraph length of individual production documents, retains complete semantic meaning of production parameters |
| Overlap Length | 100–150 characters | Connects key identifiers such as production batch numbers and tank numbers across adjacent chunks, avoids semantic fragmentation |
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. |
| Similarity Threshold | 0.72–0.80 | Filters low-relevance historical production data, ensures parameter matching accuracy for quotation calculations |
| Single Search Limit | Top 8 entries | Covers the typical historical batch sample size required for tender preparation, balances retrieval efficiency and recall completeness |
defaultToken | Set from representative sample documents | defaultToken is the default token count for direct text chunking; tune it on representative source documents. See the v4.17.2 model-field reference. It is not a vector dimension, embedding-model input cap, or retrieval/LLM context. |
| Vector Database Compatibility | Supports structured filtering and incremental writes | Matches the retrieval and update requirements of real-time multi-source data for this category, can connect to custom vector databases |
Three Common Misconfigurations
- Issue: Non-corresponding batch production data appears in retrieval results. Cause: No structured filtering conditions configured for production tank number and raw material type, leading to recall scope exceeding the sample pool required for the tender.
- Issue: Embedding results generated by the vector model have large semantic deviation from actual production parameters. Cause: No custom adaptation for smelting industry terminology such as tank voltage and anode slime content, leading to deviations in the model's semantic understanding.
- Issue: Index write timeout occurs. Cause: Incremental index mode is not enabled, and full index rebuilding is performed for real-time updated electrolyzer condition logs, exceeding system processing limits.
How to Verify Proper Configuration
- Upload a single typical electrolyzer condition log, check if chunked text retains key production identifiers such as tank number, voltage, and current.
- Submit a retrieval request that includes specified raw material ratios or furnace condition parameters, verify that the similarity of recall results falls within the preset range.
- Submit real-time updated batching list data, check if the index completes incremental writes within a reasonable time frame.
- Call the model to generate a content summary related to tender quotations, check if the returned content length matches the configured token limit.
The values provided here are common starting points, and should be evaluated against the reader's own samples.