What This Category of Data Looks Like
Non-ferrous metal smelting internal office and employee service data mainly comes from real-time production logs, equipment operation and maintenance ledgers, process operation specifications, employee job manuals, and quality inspection reports. Real-time production logs and working condition data are updated hourly. Process specifications and employee job manuals are updated quarterly or following major process adjustments. Quality inspection reports are generated upon completion of each production batch. Documents include long-text operation instructions, structured tables (with fields such as equipment number, batch number, energy consumption value, with units including kilowatt-hours, tons, ppm), and short-text operation and maintenance work orders.
What Constraints These Characteristics Impose on Vector Models and Indexes
The high-frequency updates of real-time production logs require indexes to support incremental refreshing, to avoid resource consumption caused by full reconstruction. Long-text process specifications contain a large number of continuous professional terms. When chunking, it is necessary to avoid truncating professional process names and retain contextual associations. Structured quality inspection ledgers and energy consumption tables include numerical fields with units. Indexes must support vector extraction associated with multiple fields, to avoid losing the binding relationship between numerical values and units after splitting. Short-text operation and maintenance work orders have large volume and frequent updates. It is necessary to optimize the recall speed of indexes, while retaining the association information between work order equipment numbers and fault types.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the coherence of professional terms in long-text process specifications and avoid truncating key process steps |
| Overlap Length | 100–200 characters | Retain process context between adjacent chunks and prevent professional terms from being split across chunks |
| Similarity Threshold | 0.75–0.85 | Match the semantic similarity of professional terms in industrial scenarios and filter low-relevance search results |
defaultToken | 4096 token | Adapt to the context length requirements of most industrial documents and cover the core content of complete process instructions |
maxToken | 8192 token | Support complete vector extraction of long-text quality inspection reports and operation and maintenance logs |
| Single Search Limit | Top 10 entries | Balance the comprehensiveness of search recall and system response speed |
Three Common Mistakes
- Phenomenon: When enabling the Doubao-embedding-large index model, entering a custom request address and API key, then clicking the test button directly triggers an error. Cause: The port and interface path of the custom address do not match the model call specification, or the API key has not been granted permission to call vector models.
- Phenomenon: A large number of unrelated non-target document fragments appear in search results, and the number of results exceeds the preset range. Cause: The similarity threshold is not set correctly, or the chunk length is too short, resulting in excessive semantic fragments and generating irrelevant matching entries.
- Phenomenon: After uploading table data, search fails to match numerical values or field information within the table. Cause: The multi-vector support function of the knowledge base is not enabled, or structured field associations of the table are not retained during chunking.
How to Confirm Proper Configuration
- Navigate to the vector model configuration page of the knowledge base, click the test button, and confirm that a connection successful prompt without errors is returned.
- Upload a typical process specification document, search for professional terms within the document, and verify that the chunk length of returned results matches the preset value.
- Upload structured table data, search for specific field values in the table, and confirm that results include the associated information of the corresponding table.
- View the index refresh log, and confirm that frequently updated production logs complete incremental refresh at the preset frequency.
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.