Conversation Logs and Auditing for Condiment Investment

What Data This Category Contains

Data sources for condiment investment research include publicly available research documents from the China Condiment Association, quarterly financial reports of leading enterprises, POS collection data from offline retail terminals, and shipment ledgers from supply chain logistics. Update cycles fall into three categories:

Single-product production and sales data is updated weekly
Overall industry reports are released quarterly
Corporate financial reports are updated quarterly and annually

Document structure splits into two types: structured data tables and unstructured text. Structured tables include SKU_ID, product name, ex-factory unit price (unit: yuan/500g), monthly shipment volume (unit: tons), and number of cooperating distributors. Unstructured text includes industry policy interpretations and records of competitor marketing activities.

Constraints Imposed on Conversation Logs and Auditing

The multi-frequency updates, mixed document structure, and unit-attached field characteristics of condiment investment research data impose three constraints on conversation logs and auditing:

Record the dataset version identifier for each retrieval call. This prevents data inconsistency when retrospectively reviewing investment research conclusions after data updates.
Store both retrieved SKU field values and unstructured text fragments in a structured manner. This fully preserves unit-attached field information such as ex-factory unit price and monthly shipment volume.
Bind the session ID to retrieval requests for specific SKUs. This enables quick location of investment research conversation trajectories for individual products during audits.

Configuration Settings

The following table outlines recommended configuration values and their rationales:

Configuration Item	Recommended Value	Rationale
`log_retention_days`	`90 days`	Covers a full quarterly audit cycle, and complies with log retention requirements of most industry regulations
`session_id_bind_field`	`SKU_ID`	Condiment investment research conversations mostly revolve around specific SKUs. Binding this field allows quick retrospective review of conversation logs by single product
`log_include_raw_data`	`Enabled`	Full retention of original structured field values and units is required to avoid loss of critical information during audits
`mongodb_log_batch_size`	`100 entries per batch`	Balances log write performance and storage overhead under high-frequency data updates
`audit_query_default_filter`	`By session creation time + SKU_ID`	Adapts to the core requirement of locating audit logs by time period and single product in investment research scenarios

The parameter values provided on this page are general 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, and it is recommended to test on your own samples before finalizing settings.

Three Common Misconfigurations

Symptom: After calling a workflow to generate an investment research conversation, the workflow_run_data field in the conversation log is empty. Cause: The log_include_raw_data configuration is not enabled, so raw data from workflow runs is not written to the log.
Symptom: When retrieving knowledge base content for a specified SKU, the returned results do not match the ex-factory unit price field of the target product. Cause: No SKU binding rule is configured in audit_query_default_filter, so logs are not associated with the corresponding field.
Symptom: When attempting to delete logs for a specified session, MongoDB returns an E11000 duplicate key error error. Cause: The combination of session ID and SKU_ID is not used as the deletion condition, leading to operation conflicts.

How to Verify Proper Configuration

Initiate an investment research conversation targeting a specific SKU, and check if the corresponding SKU_ID field is bound in the log details page.
Call the log export interface, and confirm that the returned results include the complete content of the workflow_run_data field.
Configure the filter conditions for audit queries, and verify that target logs can be quickly located by session creation time and SKU_ID.
Attempt to delete logs for a specified session, confirm that the operation succeeds with no errors.

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.