What Data for This Category Looks Like
Document data for mineral mining and beneficiation mainly comes from mining design plans, mineral processing procedure specifications, on-site safety inspection checklists, monthly production reports, and geological exploration reports. Data update cadence varies by type: geological exploration reports are updated with project exploration progress, production reports are updated daily or monthly, and procedure specifications are only revised when processes are adjusted. Most documents are a mix of structured and semi-structured formats, containing fixed fields such as mineral name, mining area number, grade value, and operation duration. Units mostly use industry standard metric expressions including tons, grams per ton, meters, and other standard industry measurements.
What Constraints Do These Characteristics Impose on the Vector Model and Indexing Process
The characteristics of mineral mining and beneficiation document data impose multiple constraints on the vector model and indexing process. Structured fields make up a large share of the data. Clearly distinguish between text description and numerical fields to avoid embedding numerical parameters incorrectly into the text vectorization process. The paragraph boundaries of semi-structured documents shift with process details. Ensure process descriptions remain intact during chunking, to avoid breaking logical connections. Frequently updated production reports require indexes that support incremental synchronization. Longer geological exploration reports need compatible chunking thresholds for long texts.
How to Set the Configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of single-paragraph process descriptions in mineral mining and beneficiation documents, avoiding splitting that breaks process logic |
| Overlap Length | 100–200 characters | Mitigates boundary breaks in long text chunking, ensuring semantic coherence across chunks |
| Similarity Threshold | 0.72–0.80 | Adapts to the semantic matching accuracy of technical terms in mineral mining and beneficiation documents, filtering low-relevance search results |
| Single Search Limit | Top 8 results | Balances search efficiency and recall coverage, adapting to the retrieval needs of technical documents |
maxToken | 4096 token | Adapts to the context limits of mainstream embedding models, avoiding long text chunks exceeding model processing range |
defaultToken | Calibrated via actual testing | Adapts to default request parameter values for locally deployed or custom embedding services, compatible with different deployment environments |
Three Common Misconfigurations
- Symptom: Clicking the embedding model test button directly returns an error, prompting invalid request parameters. Cause: The request address and apikey for the custom embedding service were not filled in correctly, or the parameter format does not meet the service requirements.
- Symptom: The knowledge base is configured and the chat agent test passes, but after refreshing the page, a prompt appears indicating no available index model is detected. Cause: The embedding model was not set as the default index model, or the system cache did not synchronize the latest configuration status in time.
- Symptom: Retrieval results for tabular documents fail to match queries related to numerical fields, or table structure information is lost after vectorization. Cause: Multi-vector support for tabular data was not enabled, and separate vectorization rules were not configured for structured fields.
How to Verify Successful Configuration
- Navigate to the embedding model configuration page of the knowledge base, confirm that the target embedding model is enabled, and that the request address and apikey are filled in correctly.
- Upload a typical mineral mining and beneficiation document, perform manual chunking and vectorization tasks, and check whether the chunking results match the preset length and overlap rules.
- Initiate a retrieval test based on this knowledge base, and verify whether the number of returned results and similarity match the expected configuration.
- Check the vector index status of the knowledge base, confirm that the index construction progress is complete, and there are no failed logs.
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.