What Data for This Category Looks Like
Data for this category comes from DCS/SIS control systems, online analyzers, LIMS laboratory management systems, equipment condition monitoring modules, energy metering systems, and raw material tank account books. Daily documents include operating procedures, process index cards, DCS trend data, laboratory reports, interlock bypass records, special work permits, and shift daily reports. Data update frequencies include real-time, shift handover, and daily summary. Document fields include equipment operating parameters, material concentration, operating thresholds, alarm records, and more. Most parameter units use industrial standard units such as MPa, ℃, m³/h.
Constraints on Vector Models and Indexes
Multiple heterogeneous data sources and differentiated update rhythms require the indexing link to support flexible synchronization strategies. These strategies must adapt to the distinct update frequencies of real-time data streams, shift batch data, and daily summary data. Diverse document types include structured parameter cards, semi-structured trend data, and unstructured work records. Adaptable chunking rules must be designed for different document structures to avoid splitting that breaks parameter associations or paragraph integrity. Drafting process-related exception reports relies on accurate interlock records and operating threshold matching. Index recall must prioritize associating core process fields, while ensuring high recall coverage to avoid missing key disposal references.
Configuration Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the paragraph and parameter block lengths of basic chemical process documents, avoiding splitting critical process parameter groups |
| Overlap Length | 100–150 characters | Ensures contextual coherence of adjacent chunks for process content, preventing loss of cross-chunk parameters |
maxToken | 4096 token | Adapts to the content length of long process documents, meeting the complete information requirements of vector encoding |
defaultToken | 1024 token | Balances contextual recall for exception report drafting and computing resource consumption |
| Similarity Threshold | Calibrated via actual testing | Process parameter thresholds vary significantly across enterprises, requiring adjustment based on actual business scenarios |
| Single Search Limit | Top 10 results | Covers sufficient process-related documents while avoiding redundant results interfering with drafting |
| Reranking Model | Privately deployed reranking model | Ensures accurate sorting of process-related content, adapting to the unique characteristics of enterprise-owned data |
Three Common Misconfigurations
- Issue: Choosing a generic public model when configuring the reranking model, resulting in redundant content in exception reports that does not match enterprise processes. Cause: The reranking model was not adjusted for the exclusive process data of continuous basic chemical production, and generic models cannot prioritize recalling core content such as interlock records and process thresholds.
- Issue: Setting the single search limit to top 3 results, leading to failure to recall critical interlock bypass records or historical exception reports. Cause: The distribution of exception-related content across multiple basic chemical documents was not considered, and an overly low search limit misses key references.
- Issue: Setting
maxTokento 2048 token, resulting in truncation of long DCS trend data, with critical parameter information lost during vector encoding. Cause: ThemaxTokenparameter was not adjusted based on the content length of basic chemical process documents, truncating core data that impacts exception judgment.
How to Verify Successful Configuration
- Import a typical process index card and exception report draft, check if the chunked document fragments retain complete parameter thresholds and operating steps.
- Initiate a simulated exception query, verify that the recalled documents include core content such as interlock bypass records and historical exception disposal records.
- Review the document metadata after vector encoding, confirm that data sources with different update frequencies have been correctly synchronized to the index library.
- Compare recall result rankings before and after enabling the reranking model, confirm that the sorting of process-related content aligns with business expectations.
The values provided are common starting points and should be measured against the reader's own samples.