What the data for this category looks like
Refining and chemical research report data primarily comes from public reports issued by industry associations, quarterly disclosure documents of refining and chemical enterprises, and special surveys from third-party industry consulting institutions. Update cycles are adjusted based on project milestones and industry regular meetings. Temporary additional updates are added for major plant commissioning or price adjustment events. Most documents combine structured reports and textual analysis, including fields such as plant production capacity, raw material ratio, product yield, unit energy consumption, and monthly processing volume. Units mostly adhere to industrial measurement standards including tons per year, kilograms of standard coal per ton, yuan per ton, and similar industry metrics.
What constraints these characteristics impose on model access and configuration
The structured fields in refining and chemical research reports are numerous and include specialized industrial units. This requires configuring field mapping rules for industry-specific terminology to prevent mismatches between numerical values and units. Non-fixed update cycles require flexible data source trigger mechanisms to accommodate temporarily added research report updates. Document length varies widely, ranging from a few pages of special analysis to dozens of pages of feasibility study reports. This requires configuring adaptive segmentation parameters to avoid truncation of critical business data. Industrial units have regional differences, so pre-configured unit conversion rules must be set to ensure the model recognizes a unified measurement standard and avoids recognition errors in cross-regional reports.
How to configure the settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_FIELD_MAPPING | Capacity: Plant Design Capacity, Energy Consumption: Unit Processing Energy Consumption | Adapt to the naming conventions of specialized fields in refining and chemical research reports, enabling the model to accurately extract business-related data |
maxContext | 8000–12000 characters | The length of single-section analysis or feasibility study excerpts from refining and chemical research reports typically falls within this range, avoiding truncation of core information |
RECALL_TOP_K | Top 8–12 entries | Core business information in refining and chemical research reports is concentrated. Excessive recall will introduce redundant non-critical content |
PROMPT_TEMPLATE | Fixed industry-specific prompt | Force the model to use refining and chemical industry terminology when answering, avoiding confusion between general terms and business scenarios |
SOURCE_TRIGGER_MODE | Event-triggered | Adapt to the non-fixed update cycle of refining and chemical research reports, replacing the inefficient fixed scheduled pull mode |
UNIT_CONVERSION_RULE | Conversion according to national standard industrial units | Unify measurement standards for cross-regional reports in the refining and chemical industry, ensuring consistency in the model's recognition of numerical values |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require on-site analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- After calling the model interface, the returned results do not include thought chain intermediate processes, only the final answer. The
enable_thoughtparameter is not enabled in the model access configuration, causing the interface to not return thought chain content. - In FastGPT 4.9.7, after configuring multi-channel models, the application cannot select the specified Deepseek channel model during invocation. The channel model is not associated with the application's AI model group, or the API key configured for the channel has not passed validity verification.
- An empty field error log appears when parsing refining and chemical research reports. The
PARSE_FIELD_MAPPINGparameter is not configured, so the model cannot recognize specialized fields in the research reports, leading to failure to extract critical business data.
How to verify successful configuration
- Initiate a parsing test for a single refining and chemical research report, check whether the extracted fields and units match the original text, and confirm that the field mapping and unit conversion rules are effective.
- Call the model interface, verify whether the expected thought chain content is returned if the corresponding configuration is enabled, and confirm that the
enable_thoughtparameter is configured correctly. - After configuring multi-channel models, enter the application's AI model settings page, confirm that the target channel model appears in the optional list, and verify that the channel association logic is effective.
- Submit refining and chemical research reports of different lengths, check whether the parsing and recall results meet business expectations, and confirm that the context length and recall count parameters are adapted to the current scenario.
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.