What This Category’s Data Looks Like
Auto parts profit margin and market trend data comes primarily from supply chain settlement ledgers, original equipment manufacturer (OEM) supporting quotation systems, shipping profit reports from parts manufacturers, and commodity trading platform market data for corresponding raw materials. Two data update schedules are used: profit margin daily reports are updated in batches every early morning, while raw material market data is synced hourly. Data is stored as structured tables. Core fields include part SKU code, supplier name, unit cost, ex-factory selling price, and compatible vehicle model information. Units include yuan per piece, ten thousand yuan per batch, and similar units.
Constraints Imposed by These Characteristics on Conversation Logging and Auditing
Because the data includes detailed fields such as SKU code and compatible vehicle model, conversation logs must associate these fields to support audit tracing by individual part. Data sources with multiple update frequencies require logs to bind data version numbers and query timestamps. This prevents expired data from being used during audits. The structured table format means logs cannot store only scattered text summaries. Full standardized fields must be extracted and stored to enable direct field-based filtering and statistics during audits. Additionally, compliance requirements for automotive supply chains mandate complete conversation context retention. Logs must record full query requests and associated data links, not just final reply content.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
LOG_RETENTION_DAYS | 180 days | Auto parts supply chain audits require retention of conversation and data association logs for at least six months to meet industry compliance tracing requirements |
DB_INDEX_FIELDS | ["sku_code", "supplier_name", "query_time"] | Add core field indexes to the conversation log database to speed up audit retrieval by part SKU, supplier, and query time |
PARSE_STRUCTURED_TABLE | enabled | For the structured table format of auto parts profit margin daily reports, enable automatic field extraction to ensure logs store complete standardized data |
API_REQUEST_TIMEOUT | 30 seconds | Match the timeout threshold for part data source interfaces and MongoDB writes to avoid log errors caused by slow operations |
maxContext | previous 6 rounds of conversation | Limit the context length of audit logs to balance information completeness and storage resource usage |
SSE_CONNECTION_TIMEOUT | 60 seconds | Adapt to audit log storage in long conversation scenarios, preventing loss of unsaved conversation data due to client disconnection |
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. It is recommended to test against your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: When parsing auto parts profit margin daily reports in a Docker deployment environment, logs repeatedly report slow operation xxxxms and MongoDB connection verification failures. Cause:
DB_INDEX_FIELDSis not configured to include thesku_codefield. This causes MongoDB to perform full table scans when retrieving part data, triggering slow operations and connection timeouts. - Symptom: After generating a publicly accessible link, chat records cannot be filtered and queried by the specified part SKU. Cause: The
PARSE_STRUCTURED_TABLEconfiguration is not enabled. SKU code fields are not extracted from knowledge base documents, so logs do not associate the corresponding audit dimensions. - Symptom: Unfinished conversation content is not saved to the database after the client closes the SSE connection. Cause:
SSE_CONNECTION_TIMEOUTis not set to a reasonable threshold, or the conversation log breakpoint continuation mechanism is not enabled, resulting in loss of unsubmitted request data.
How to Confirm Configuration Is Complete
- Log in to the FastGPT backend conversation log management page, enter the specified part SKU code, and verify that associated conversation records can be retrieved quickly.
- Upload a standardized auto parts profit margin daily report table, check the parsed field list, and confirm that core preset fields including SKU code and supplier name are included.
- Initiate a conversation with multiple rounds of part queries, close the client connection before the AI finishes replying, re-enter the session, and verify that unfinished conversation content has been saved to the database.
- Log in to the MongoDB management interface, check the index list of the conversation log database, and confirm that indexes have been created for the
sku_code,supplier_name, andquery_timefields.
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.