What the data for this category looks like
Data sources for this category include DCS/SIS systems, online analyzers, LIMS laboratory management systems, equipment condition monitoring modules, energy metering devices, and raw material tank data terminals. Data update cycles cover real-time time-series collection, shift handover summaries, and daily report archiving. Daily documents include operating procedures, process indicator cards, DCS trend curves, laboratory reports, interlock bypass records, special work permits, and shift production logs. Document fields often have clear units: reaction temperature in degrees Celsius, feed flow rate in tons per hour, and pressure in megapascals.
What constraints do these characteristics impose on the vector model and index link
Short-interval sampling of real-time time-series documents generates large volumes of fragmented data. This requires vector models and indexes to support fast embedding and storage of high-frequency, small-batch data. Batch documents archived per shift or day require index refresh strategies aligned with fixed update cycles. This prevents indexes from becoming out of sync with source data. High-risk documents such as interlock bypass records and special work permits must be configured with higher recall priority to prevent omission of critical information. Fields with multiple units increase the feature dimension of text embedding. Select a vector model adapted to structured numerical text, and adjust the defaultToken and maxToken parameters to match document characteristics, avoiding embedding bias.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the text length of process descriptions and interlock rules in basic chemical documents, avoiding cutting that breaks logical units |
| Overlap Length | 100–150 characters | Covers cross-chunk process parameter association information, preventing key logic from being truncated by chunking |
| Similarity Threshold | 0.72–0.85 | Filters low-match irrelevant documents while preserving recall rates for high-risk interlock-related content |
| Single Search Limit | Top 10 entries | Adapts to the need for multi-document reference when preparing bid documents, avoiding interference from excessive redundant information |
maxToken | 8000 | Meets the context limit of FastGPT v4.17.0, supporting complete process indicators and job requirements |
defaultToken | Calibrated via actual testing | Adapts to the field unit embedding requirements of basic chemical documents, optimizing vector representation of structured text |
Three common mistakes
- Critical interlock bypass record content is missing from search results, with insufficient recall rate. The cause is failure to set high-risk documents as priority recall items, and excessively long chunk length leading to critical rules being cut off.
- The index model configuration entry cannot be found in the interface, with an error prompt indicating no available index channel. The cause is failure to enable the local index storage path in FastGPT v4.17.0 system settings, or failure to load the adapted embedding model plugin.
- Knowledge base disk usage exceeds expectations, including redundant data beyond original files, split chunks, and embedded vectors. The cause is failure to configure an automatic index cleanup policy, leading to delayed deletion of expired process trend data indexes.
How to confirm the configuration is correct
- Upload a typical process indicator card document, check that the split text retains complete parameter and unit information, with no obvious cutting breaks.
- Enter the search term "interlock bypass requirements for a certain reaction unit", verify that the number of returned results matches the single search limit setting, and includes the target interlock record.
- View the system disk usage statistics, confirm that the knowledge base usage only includes three categories of data: original files, split text chunks, and embedded vectors, with no additional redundant cache.
- On the FastGPT v4.17.0 index management page, confirm that the loaded vector model meets the embedding requirements for structured chemical texts.
The values provided are common starting points and should be evaluated against the reader's own samples.