What data for this category looks like
Refining and chemical investment research data primarily comes from real-time operating data of distributed control systems (DCS) in refining and chemical units, process flow diagrams (PFD), material balance reports, quality inspection analysis reports, as well as industry-wide process specifications and accident case documents. Real-time operating data updates at minute-level intervals. Report-style documents update daily, weekly, or monthly. Process manuals and industry specifications receive static updates. The data structure includes structured process parameters such as reaction temperature, tower pressure, and material flow, with corresponding units of ℃, MPa, and m³/h. It also includes unstructured long documents such as unit operation procedures and process optimization plans.
What constraints these characteristics impose on multi-turn dialogue and prompting
The minute-level update of real-time operating data requires multi-turn dialogue to retain parameter context from recent interactions, to avoid process judgment deviations caused by lost context. The coexistence of structured parameters and unstructured long documents requires prompting to distinguish recall rules for the two types of data, prioritizing matching the currently discussed process unit. The continuous process descriptions in long documents require retaining the relevance of unit operations during segmentation, to avoid logical breaks caused by truncation. Unit differences across data sources require prompting to include built-in unit conversion rules, ensuring accurate parameter comparison across data sources.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Refining and chemical knowledge bases contain content combining long process descriptions and real-time operating data, requiring retention of process parameter context across multi-turn interactions |
recall_count | 6–10 entries | Refining and chemical knowledge bases cover multi-dimensional process data; sufficient recall volume can cover associated information across different process units |
similarity_threshold | 0.75–0.85 | Distinguish precise matching and fuzzy association of process parameters, avoiding irrelevant operating data from being included in dialogue results |
chunk_max_length | 1000–1500 characters | Refining and chemical process documents include continuous process descriptions; segmentation must retain the integrity of unit operations |
parse_timeout | 300 seconds | Large-scale refining and chemical unit documents have significant file size; parsing requires sufficient time to complete structured extraction |
enable_multi_round_memory | Enabled | Retain process parameters and operating context across multi-turn interactions, avoiding repeated queries of basic parameters |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test against in-house samples before finalizing settings.
Three common mistakes
- Symptom: After a workflow runs to the AI dialogue node, it automatically jumps back to the question classification node, and cannot continue the current refining and chemical process discussion. Cause: No skip-classification jump rule is configured after the workflow's dialogue node, causing the classification logic to re-trigger every time the AI returns a response.
- Symptom: For refining and chemical process parameter queries orchestrated via HTTP requests, the returned output field is not displayed in the dialogue interface. This issue occurs frequently in version 4.6.9. Cause: The
output_visibleswitch is not enabled in thehttp_response_parseconfiguration. This parameter is disabled by default in this version. - Symptom: When calling the Gemini model, image files such as refining and chemical process flow diagrams cannot be uploaded, and only text dialogue is supported. Cause: Full-modal invocation configuration for the model is not enabled, or multi-modal input support is not explicitly specified in the prompting.
How to confirm configurations are set correctly
- Initiate multi-turn queries containing continuous process parameters, verify that context is retained, and that no parameter loss or repeated queries occur.
- Upload a single large refining and chemical unit document, verify that parsing time does not exceed the configured
parse_timeoutparameter, and that no parsing failure errors appear. - Initiate structured process parameter queries, verify that the number of recalled documents and similarity threshold matching results align with preset recall rules.
- Test the workflow orchestrated via HTTP requests, verify that the returned output field is normally displayed in the dialogue interface.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.