Reference only; verify against the target FastGPT release and deployment documentation.
What data for this category looks like
The declaration materials and case review data for iron and steel smelting mainly come from smelting production ledgers, raw material quality inspection reports, project declaration approval forms, and environmental protection filing cases. Individual declaration tasks trigger data updates. Teams batch-import materials generated within a single declaration cycle into storage. Archived cases receive batch updates quarterly. Document structures include professional fields such as furnace number, smelting batch, ton steel energy consumption, and environmental protection emission values. Some cases contain multi-page rectification records and process approval nodes, with single-document lengths varying widely.
What constraints these characteristics impose on vector models and indexing
Iron and steel smelting declaration materials and case reviews contain many professional fields and units. Using too short chunk lengths splits complete technical terms, leading to distorted embedding semantics. Single-document lengths vary widely, so teams must limit the token length of a single embedding to avoid truncating key approval information. Data updates mainly trigger in batches, so real-time indexing is unnecessary. Teams can use incremental indexing to reduce resource consumption. Different types of documents have obvious field differences, so teams must configure field-level index filtering to avoid invalid matching across document types.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the length of single-segment professional content in iron and steel smelting declaration materials, avoids splitting complete professional terms such as furnace number and ton steel energy consumption |
| Overlap Length | 50–80 characters | Retains process context across chunks, prevents professional process descriptions from being truncated |
maxToken | 8192 | Adapts to long paragraph content in industry documents, avoids truncating key approval opinions |
| Similarity Threshold | 0.72–0.85, calibrated via actual testing | Filters low-match interference from professional steel terms, distinguishes valid declaration materials from invalid cases |
| Single Search Limit | Top 10 results | Controls the number of returned results, aligns with the manual review rhythm of audit personnel |
defaultToken | 4096 | Unifies the embedding length of single vectors, ensures consistency of index structure |
Three common mistakes
- Similarity scores returned by vector searches fall outside the normal range, with some results showing values above 10000. This occurs when no similarity threshold filtering is configured, and normalization processing is not applied to embedding results.
- After uploading iron and steel smelting declaration materials, no corresponding embedded data appears in the vector database, and no matching results are returned during searches. This happens when the incremental index switch is not enabled, or the file format of the uploaded materials is not recognized by the supported parser.
- No m3e-related options appear in the model list, making it impossible to select the corresponding embedding model. This occurs when the access channel for the custom model is not configured according to official documentation, so the channel is not loaded.
How to confirm configurations are correct
- Upload a standard iron and steel smelting declaration material, check the background embedding log to confirm that the chunk length matches the preset value.
- Initiate an audit query containing professional terms, check whether the similarity scores of the returned results fall within the preset threshold range.
- Check the index list in the vector database to confirm that newly added declaration materials and case data have been correctly indexed.
- View the system settings page to confirm that the configured values of
defaultTokenandmaxTokenmatch the preset values.
The values given are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.