What the Data for This Category Looks Like
Data related to oil and gas extraction yield comes primarily from public reports by energy information agencies, oil and gas field production monitoring systems, and commodity trading platforms. Data is updated daily at midnight with full block data for the previous day. Documents are provided in structured JSON or CSV format. Each document includes fields such as block number, daily oil production, unit extraction cost, current spot selling price, and tax amount. Production units are cubic meters, cost units are yuan per ton, selling price units are US dollars per barrel, and no unified yield metric is pre-calculated.
What Constraints These Characteristics Impose on Workflow Orchestration
The need to pull data from multiple sources requires configuring cross-source data aggregation nodes in the workflow to connect public data sources and internal production systems separately. The daily update rhythm requires setting timed triggers for the workflow, with execution windows that avoid peak data update periods to prevent incomplete data pulls. The inconsistent field units and naming conventions require configuring pre-unit conversion and field mapping nodes to unify calculation standards. The logic of independent calculation per block requires configuring loop nodes to iterate through all block data, avoiding error accumulation from batch calculations.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
schedule_cron | 0 2 * * * | Matches the daily midnight data update rhythm of data sources, avoids peak hours |
http_request_timeout | 120 seconds | Accommodates the time required for multi-source data pulls, prevents mid-execution interruptions |
field_mapping_config | Map block_prod_volume → production_tons, spot_price_usd → spot_price_cny | Unifies field units and naming from different sources to ensure consistent calculation standards |
loop_max_iterations | 50 times | Controls the upper limit of block count per workflow execution, avoids excessive node load |
retry_strategy_max_retries | 2 times | Addresses temporary network fluctuations or temporary unavailability of data sources |
ai_node_context_window | 8000 characters | Accommodates the context length requirements of multi-block data and market data |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on internal samples before finalizing settings.
Three Common Mistakes
- Symptom: The
chat_completionnode returns theLLM_MODEL_RESPONSE_EMPTYerror code, with empty output fields. Cause: No reasonable value is configured forai_node_context_window, and the multi-block data passed exceeds the model's context limit, triggering an empty response. - Symptom: The
condition_nodeshows the input boolean value astrue, but the downstream branch does not trigger execution. Cause: The judgment matching rule is not configured correctly, and a string type"true"is compared with a boolean typetrue, resulting in a match failure. - Symptom: After associating a knowledge base with the
tool_node, oil and gas extraction cost-related document content cannot be retrieved. Cause: Theknowledge_base_filterparameter is not configured in thetool_node, and the industry tag for oil and gas extraction is not specified, resulting in retrieval results that do not meet requirements.
How to Confirm Proper Configuration
- Trigger a single execution of the workflow, check the output of the
data_pull_node, and verify that field names and units match the preset mapping rules. - Input preset boolean values and comparison parameters for the
condition_node, check whether the workflow's branch execution path matches expectations. - Call the knowledge base associated with the
tool_node, enter a query related to oil and gas extraction yield, and confirm that relevant document content can be retrieved. - Check the workflow execution logs to confirm that no errors such as data conversion failure or empty model response occur. If errors appear, adjust the corresponding configuration parameters.
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.