What This Category of Data Looks Like
Refinery investment research data comes from multiple sources: real-time operating logs of refinery units, crude oil processing process specifications, batch-level product quality inspection reports, regional refined oil supply and demand weekly reports, and industry policy documents. Update rhythms vary widely: unit operating parameters refresh every minute, process specifications receive static updates, quality inspection reports are produced alongside each production batch, and industry policies and research reports have no fixed update cycle. Document structures include three types: structured parameter tables (with fields including temperature, pressure, flow, and corresponding units ℃, MPa, m³/h), semi-structured process documents, and unstructured industry analysis text.
What Constraints Do These Characteristics Impose on Conversation Logging and Auditing
The multi-tempo update patterns and mixed structure of refinery investment research data create multiple constraints for conversation logging and auditing. Minute-level real-time operating parameter refreshes require logs to accurately tie specific device nodes and timestamps to the moment of invocation, preventing data misalignment during traceability. Batch-level quality inspection reports require the auditing link to verify that conversation records are bound to production batch identifiers, ensuring compliance with data usage rules. The mix of long documents and short parameters requires logs to separate structured parameter invocation scenarios from unstructured document recall scenarios, and store matching metadata for each. References to unstructured industry policy content require audit logs to record specific fragments and source release times, meeting compliance traceability requirements.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
LOG_MAX_RETAIN_DAYS | 90 days | Refinery industry investment research data typically follows a quarterly compliance audit cycle. 90 days covers regular compliance check requirements while controlling storage costs. |
AUDIT_TRACE_ENABLE | Enabled | Refinery investment research involves process parameters and production data. Full invocation link recording ensures traceability of input and output for every conversation. |
EMBEDDING_BATCH_SIZE | 4–8 entries | Refinery documents include long-text process specifications and short parameter entries. A smaller batch size reduces the risk of exceeding embedding rate limits, adapting to batch processing rhythms. |
MAX_CONTEXT_TOKENS | 16000–24000 | Refinery process documents are typically lengthy. Sufficient context must be retained to ensure coherent conversation logic, while avoiding exceeding model context limits. |
LOG_RECORD_FIELD_WHITELIST | ["user_id", "request_time", "device_node", "batch_id", "response_content"] | Refinery investment research requires tracking calling subjects, time, device nodes, production batches, and output results. The whitelist filters irrelevant fields and simplifies the auditing process. |
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 your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: The Human field is empty in the API conversation response record preview. Cause: The user identification parameter was not correctly passed in the request body, or the parameter format did not meet platform specifications, resulting in the log failing to capture user input content.
- Phenomenon: Vectorization tasks report errors, and logs prompt that the embedding rate limit is exceeded, with tasks remaining in the queue for a long time. Cause: The
EMBEDDING_BATCH_SIZEparameter was not adjusted, and default high-concurrency batch processing was used for refinery long documents, exceeding third-party interface rate limits. - Phenomenon: In v4.9.0, workflow runs generate untriggered gpt-4o-mini error logs. Cause: A removed gpt-4o-mini node remains in the workflow configuration, or cached node metadata has not been updated, triggering invalid invocation verification.
How to Confirm Configuration is Correct
- Log in to the log management interface, view API call logs from the last 1 hour, confirm that each record contains preset fields, and verify that the field whitelist configuration is effective.
- Submit a batch of test data including long process documents and short parameter entries, run the vectorization task, check the task queue status, confirm that no rate limit exceeded errors occur, and verify that the batch configuration is reasonable.
- Trigger a conversation that includes a query for refinery process parameters, check the audit trace link, confirm that input parameters, calling nodes, and output results are fully recorded, and verify that the audit tracing function is enabled.
- View the log retention period statistics panel, confirm that automatic log deletion times match the preset configuration, and verify that the storage strategy is effective.
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.