What the data for this category looks like
Typical subjects of this category are basic chemical enterprises producing chlor-alkali, synthetic ammonia, methanol, soda ash, and similar products. Daily documents cover operating procedures, process index cards, DCS trends, laboratory reports, interlock bypass records, special work permits, and shift reports. Data sources include DCS/SIS systems, online analyzers, LIMS systems, equipment condition monitoring tools, energy metering platforms, and raw material storage tank monitoring systems. Data updates follow three rhythms: real-time (for example, DCS trends), per shift (for example, shift reports), and daily (for example, laboratory reports). Documents include both structured fields (such as temperature, pressure setpoints and measured values on process index cards, with units of ℃, MPa, m³/h) and unstructured text (such as operating steps on special work permits). This category involves bulk continuous production, with prominent hazardous media and strong interlock control, and differs from the crude oil multi-component fractionation network used in petroleum refining.
What constraints these characteristics impose on the vector models and indexing link
Data for this category is multi-source heterogeneous and has significantly different update frequencies. This requires indexing systems to support incremental updates and multi-source data adaptation. Documents contain highly sensitive strong interlock parameters and real-time time-series data. Errors can lead to toxic and flammable material leaks, plant shutdowns, or product contamination, so confidence requirements for recall results are higher. Document lengths vary widely, from short texts like process index cards to long time-series DCS trend files, so flexible chunking strategies are needed. Some data (such as interlock bypass records) requires strict matching of specific fields, which places additional constraints on vector recall precision.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the paragraph length of process procedures and the time-series block length of DCS trends, avoiding overloading single chunks |
| Overlap Length | 100–150 characters | Prevents key interlock parameters on process index cards from being truncated during chunking, ensuring complete recall |
| Similarity Threshold | 0.85–0.90 | Meets high-confidence recall requirements for strong interlock scenarios in basic chemical production, filtering low-correlation results |
| Single Search Limit | Top 6 results | Covers the recall scope of multiple documents such as shift reports and interlock bypass records, meeting information needs for on-site Q&A |
| maxToken | 4000 | Adapts to the total length requirements of basic chemical documents, following the general specification for the maxToken parameter |
| Timeout Setting | 600 seconds | Adapts to the indexing processing time of large-volume data such as DCS trends, preventing task interruptions mid-run |
Three common mistakes
- Symptom: Indexing tasks remain in a running state with no progress, or return a 503 status code. Cause: The system is not adapted to the real-time updated DCS data of basic chemical production, and incremental update trigger rules are not configured, leading to repeated loading of large-volume data that occupies system resources.
- Symptom: The embedding model call succeeds, but the Q&A returns no matching results. Cause: The similarity threshold is set too low, incorrectly identifying irrelevant process parameter data as matching items, or the chunk length is too long, truncating key interlock parameters.
- Symptom: The conversation model runs normally, but the vector indexing task shows no progress. Cause: API information for the embedding model is not configured separately, and only the conversation model's key is bound, preventing the vector indexing link from calling the corresponding model.
How to confirm proper configuration
- View the indexing task logs in FastGPT v4.17.0, confirm there are no timeout errors or connection failure records, and that the number of chunked documents matches the number of source documents.
- Manually enter process indicators or interlock-related questions, verify that the recall results include corresponding DCS trends, process index cards, or interlock bypass record content.
- Test the incremental indexing function, upload newly generated shift report data, confirm that the indexing task completes within the set update cycle.
- Check the embedding model call logs, confirm that API request parameters are correct, with no format errors or permission issues.
The values provided are common starting points and should be measured against the reader's own samples.