What this category's data looks like
Mine mining and beneficiation ledger data comes from underground mining monitoring terminals, mineral processing workshop PLC systems, equipment inspection logs, and quality inspection reports. Update frequencies range from real-time to once per hour. Some monthly summary reports are updated daily. A single ledger record includes fields such as mining location, daily mining volume, raw ore grade, mineral processing recovery rate, and equipment operating duration, with a timestamp and work shift identifier. Most fields are numeric, with units including tons, grams per ton, hours, cubic meters, and others. Documents are primarily structured tables or time-series logs. Some paper reports are scanned to image format before upload.
What constraints these characteristics impose on vector models and indexes
High-frequency updated time-series data requires indexes to support incremental writes, avoiding performance losses from full reconstruction. Structured ledgers with mixed multiple fields must retain field association relationships during chunking, to prevent splitting key identifiers such as work shifts and timestamps. Documents in scanned image format require OCR preprocessing before vector generation, increasing preprocessing step complexity. Features with large spans in numeric fields require vector models to adapt to numeric encoding logic, avoiding deviations in similarity calculations. Report generation needs to associate ledger data across multiple time periods, so indexes must support fast recall of associated records by time range.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Mine mining and beneficiation ledgers have many fields per record. Too short a chunk length splits associated fields, while too long exceeds the vector model's maxToken limit |
| Overlap Length | 100–150 characters | Timestamps and shift identifiers in time-series ledgers must be retained across chunks to avoid broken associated information |
defaultToken | 8192 | Adapts to the context window of most open-source embedding models, and fully carries the multi-field content of mine ledgers |
| Similarity Threshold | 0.72–0.80 | Numeric feature similarity differentiation in mine ledgers is relatively high. A threshold that is too low introduces irrelevant records, while a threshold that is too high misses associated data |
| Single Search Limit | Top 10 entries | Report generation covers ledger data from main operating periods; too many entries increases context redundancy |
| Maximum Context | 16000 token | Adapts to integration of multiple batches of data for report generation, and must match the defaultToken configuration |
Three common configuration mistakes
- Index creation fails when deploying a PostgreSQL database via Docker, with the interface prompting "Database connection failed". The cause is failing to map the PostgreSQL database port to the host machine, and the FastGPT container not being added to the same Docker network as the PostgreSQL database, preventing access to the index storage node.
- A 503 error is returned when calling the embedding model, with the prompt "No available channel for model text-embedding-v3 under current group default". The cause is failing to configure the embedding model separately, using the default OneAPI channel but not configuring available nodes for this model, or exhausting the channel quota.
- The configured API key fails to load after starting with docker-compose, with the interface showing unauthorized access. The cause is failing to correctly configure the CHATAPIKEY environment variable in docker-compose.yml, and not rebuilding the container for the configuration to take effect.
How to confirm configuration is valid
- Access the FastGPT knowledge base management page, select the target mine mining and beneficiation ledger knowledge base, click "Test Retrieval", input a retrieval keyword for a specified time period, and verify that returned results include the corresponding records.
- View vector model call logs, confirm that the
defaultTokenparameter of the embedding request matches the configured value, and there are no error messages about context exceeding the window. - Check Docker container logs, verify PostgreSQL database connection status, and there are no error messages such as "connection timed out" or "authentication failed".
- Manually upload a small mine mining and beneficiation ledger file, wait for index creation to complete, perform a retrieval, and confirm that the number of returned results matches the single search limit configuration.
The values provided here are common starting points, and should be evaluated against samples from the respective deployment contexts.
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.