What the data for this category looks like
Data primarily comes from geological exploration archives, monthly mining plans, mineral processing process specifications, equipment operation and maintenance logs, safety inspection records, and mining right approval documents. Geological exploration archives are static historical data. Mining plans are updated monthly. Operation and maintenance and inspection records are generated daily. Document fields include drill hole number, ore layer thickness, grade, equipment model, processing capacity, fault description, and more. Units cover professional measurement standards such as meters, tons, grams per ton, hours, and others.
What constraints these characteristics impose on the vector models and indexes link
The high proportion of static historical geological archives, monthly updates of mining plans, daily generation of operation and maintenance and inspection records, and large differences in update rhythms across different data types require the index system to support dual-mode configuration of separate static full indexes and incremental indexes. Documents contain professional terminology and multiple types of fields, so vector models need to adapt to mining-specific corpus and mixed field vectorization requirements. The length of individual documents varies widely, from a few pages of safety specifications to dozens of pages of geological reports, requiring flexible chunking adaptation rules. The timing of data updates is inconsistent, so logic to automatically trigger reindexing based on data update time must be configured.
Reference only; verify against the target FastGPT release and deployment documentation.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the length span of mining and mineral processing documents, balancing professional terminology density and vectorization efficiency |
| Overlap Length | 100–150 characters | Prevents professional terminology from being truncated during chunking, ensuring contextual coherence |
| maxToken | 4096 token | Adapts to the context splicing requirements of long document chunks, aligning with the information density of professional mining documents |
| defaultToken | 1024 token | Serves as the default token limit for vectorization, balancing the information capacity of a single vector and storage costs |
| Similarity Threshold | 0.72–0.80 | Matches the semantic similarity distribution of mining-specific corpus, filtering low-correlation recall results |
| Single Search Limit | Top 10 entries | Controls the number of initial recall results, adapting to the information screening needs of inquiry and response scenarios |
Three Common Misconfigurations
- Symptom: After manually uploading a geological exploration report, the interface shows that index generation is successful, but index entries are empty several hours later. Cause: Index persistence configuration is not enabled, and temporarily cached indexes are not written to the vector database.
- Symptom: After replacing the embedding model, the recall results for multilingual ore layer grade descriptions decrease significantly. Cause: A full re-embedding operation is not performed on existing knowledge base documents, and only the vectorization rules for newly uploaded documents are updated.
- Symptom: When searching for questions related to the grinding process, recall results show fragments where terms such as "grinding concentration" and "flotation reagent" are truncated. Cause: Chunk overlap length is set too low, leading to professional terminology being truncated during chunking and loss of complete contextual information.
How to Verify the Configuration is Correct
- Upload a typical mining and mineral processing document, view the chunked text fragments, and confirm that the length and overlap settings match expectations.
- Trigger a full re-embedding operation for existing documents, wait for the task to complete, then check the storage usage of the vector database to verify that the storage overhead of the vectorization configuration aligns with expectations.
- Initiate a test inquiry, view the number of initial recall results, and confirm that the single search limit setting takes effect.
- Check the index status several hours later, confirm that the index does not disappear automatically, and verify that the persistence configuration is working properly.
The values provided 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.