Reference only; verify against the target FastGPT release and deployment documentation.
What data in this category looks like
Data for internal office and employee services in mining and mineral processing primarily comes from mining scheduling logs, equipment maintenance records, employee attendance and scheduling, safety operation procedures, mineral processing parameter reports, trade union activity notices, and similar sources. Update rhythms vary significantly: scheduling logs and real-time operational data are updated per minute or shift, safety procedures and policy documents are updated via static revisions, and attendance records and activity notices are updated weekly or monthly. Document structures include standardized fields and free text: standardized fields such as equipment ID, runtime duration, shift, and hazard level, with units including tons, meters, hours, shifts, and similar; free text such as maintenance fault descriptions, safety training reflections, and similar.
What constraints do these characteristics impose on the vector model and indexing workflow
The data characteristics of mining and mineral processing impose multiple constraints on the vector model and indexing workflow: First, large differences in data update frequencies require mixed configuration of full indexing and incremental indexing to avoid redundant calculations for real-time data. Second, the length of single data entries spans a wide range, from dozens of characters for attendance records to thousands of characters for mineral processing procedures, requiring adaptive dynamic chunking parameters. Third, internal office data often reaches scales of over 100,000 entries, requiring adjustments to index batch processing parameters to avoid build timeouts, and adjustment of the maxToken parameter to fit model input limits. Fourth, fields contain mixed numeric and text content, requiring upfront unification of text formatting rules to ensure consistent embedding results.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Balances adaptation needs for long documents (such as mineral processing procedures) and short texts (such as maintenance logs) in mining and mineral processing |
| Overlap Length | 100–200 characters | Avoids chunk breakage of contextual coherence, adapting to continuous fault description scenarios in equipment maintenance records |
maxToken | 4096 token | Fits the input limit of most commercial embedding models, avoiding truncation of critical parameter content |
defaultToken | 2048 token | Serves as the default context length, meeting conventional information retrieval needs for employee service-related queries |
| Single Search Limit | Top 10 entries | Balances retrieval efficiency and recall coverage, adapting to common query scenarios for internal office work |
| Similarity Threshold | Calibrated via actual testing | Adapts to matching precision requirements for mine safety hazard texts, avoiding false recalls or missed recalls |
Three common errors
- Phenomenon: After importing 100,000 pieces of CSV-formatted maintenance data, the total number of embedded data entries decreases by thousands. Cause: Some text fields contain unparseable special characters (such as blasting parameter symbols in mine blasting records), no pre-emptive text cleaning was performed, leading to embedding failures and automatic filtering.
- Phenomenon: Collection creation succeeds, but page indexing never builds. Cause: Incremental indexing trigger logic is not correctly bound to data update events, or index shard configuration exceeds cluster storage limits, causing background index build tasks to block and not execute.
- Phenomenon: Vector indexing works normally locally, but vector scores are completely identical after Docker packaging and deployment. Cause: The embedding model’s API key environment variable is not correctly configured in the Docker image, causing all requests to call the default test interface and return fixed vector results.
How to confirm configurations are set correctly
- Verify that the total number of embedded data entries matches the source data volume, with no obvious missing entries.
- Test chunking effects for different text types (such as short attendance records, long mineral processing procedures) to confirm no excessive truncation or redundant chunks.
- Compare vector score differences between local and Docker deployment environments to confirm configuration parameters are synchronized and consistent.
- Verify response times for different retrieval modes to confirm they meet performance threshold requirements expected by the business.
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.