What the data for this category looks like
Business data in the petroleum refining sector comes from DCS/APC, LIMS, tank farm metering, crude oil evaluation, equipment management systems, and energy systems. Update frequencies cover real-time, shift-based, and daily cycles. Daily business documents include operating procedures, production plans, material balance reports, distillate export quality reports, interlock logs, shift handover records, and change management documents. Document fields cover raw material property parameters, unit operation parameters, quality inspection indicators, equipment status identifiers, and more. Common industrial units such as mass, volume, temperature, and pressure are used for corresponding measurements.
What constraints do these characteristics impose on vector models and indexing?
Multiple heterogeneous data sources require the indexing system to support mixed input of structured reports and unstructured process documents. This avoids format parsing errors. Differentiated update frequencies require the index to support parallel incremental synchronization and batch update modes. These modes adapt to synchronization needs for real-time process data and daily reports respectively. Bid documents need to link to precise business data such as crude oil properties and unit load. This requires the vector index to support semantic matching of specific business fields, ensuring retrieved content aligns with production parameters required for bids. Coupled multi-unit business logic requires the index to retain cross-document process association features. This avoids retrieval of isolated single-unit data, ensuring the completeness of bid proposals.
Configuration Settings
This configuration applies to FastGPT v4.17.0. Specific settings are listed below:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the typical length of single-section operating instructions and material balance tables in refinery process documents, to avoid semantic fragmentation |
| Overlap Length | 100–150 characters | Covers cross-chunk process association logic, such as the connection between interlock conditions and operating steps |
maxToken | 4096 token | Adapts to the input upper limit of the bce-embedding-v1 model, and is compatible with the total length of long documents after chunking |
| Similarity Threshold | 0.75–0.85 | Filters low-match non-process-related documents, ensuring retrieved content aligns with production parameters required for bids |
| Single Search Limit | Top 8 entries | Balances retrieval accuracy and the length limit of bid documents, to avoid mixing redundant data |
defaultToken | Set from representative sample documents | defaultToken is the default token count for direct text chunking; tune it on representative source documents. See the v4.17.2 model-field reference. It is not a vector dimension, embedding-model input cap, or retrieval/LLM context. |
Three Common Misconfigurations
- After uploading data using the chunk mode pushdata API, the interface continuously shows no change in the indexing status. The index synchronization rule corresponding to the data update frequency is not configured. Real-time data from multiple systems accumulates, causing the indexing queue to block.
- The embedding model call returns the error: This token does not have permission to use the model: text-embedding-3-large. The cause is failure to switch to the compatible bce-embedding-v1 model. It can also be caused by failure to correctly configure access key permissions for the embedding model.
- Indexed retrieval results contain a large number of non-process-related administrative documents. These results cannot match the production parameters required for bids. The cause is that the chunk length is set too long, leading to incorrect semantic segmentation. It can also be caused by a similarity threshold set too low, resulting in retrieval of low-match irrelevant data.
How to Verify Successful Configuration
- Upload a single crude oil evaluation document. Check whether the index completion status updates within 10 minutes. This matches the synchronization requirements of real-time data.
- Call the embedding model test interface. Input a fragment of a material balance table. Confirm that no token overflow error is returned for the vector data. This matches the
maxTokenconfiguration value. - Initiate a retrieval for "production plan required for bid quotation of a certain crude oil grade". Check whether the retrieval results include associated documents for corresponding unit load and distillate export quality indicators.
- View the indexing queue monitoring panel. Confirm that there are no accumulation errors during multi-system data synchronization. This meets the update frequency requirements of real-time, shift-based, and daily updates.
The values provided here are common starting points, and should be evaluated against the reader's own samples.