What data for this category looks like
Plastics and rubber investment research data comes from public market data of domestic futures exchanges, monthly supply and demand reports from industry associations, and daily price quote documents from spot traders. Update cycles range from real-time to monthly. Most structured documents include fields such as product name, specification model, origin, price unit (yuan/ton), supply and demand gap, and capacity utilization rate. Unstructured research report documents contain multi-dimensional market analysis paragraphs.
What constraints do these characteristics impose on the conversation logging and auditing workflow
Multiple update cycle data sources require logs to be categorized by collection time. This prevents audit deviations caused by mixed cross-version data. Structured fields have clear units. Audits must verify unit consistency during parameter transfer. This prevents conversation result deviations from unit conversion errors. Multi-source data has complex call chains. Logs must bind session IDs and each interface's return identifier. This enables easy tracing of abnormal links in complete investment research conversations.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
log_retention_days | 90 days | Matches the standard audit cycle for the investment research industry, meets compliance traceability requirements |
embedding_batch_size | 32–64 items/batch | Balances vectorization efficiency and rate limits, adapts to differences in data length for plastics and rubber data |
audit_session_bind | Enabled | Binds session IDs to all associated call logs, facilitating tracing of complete investment research conversation links |
log_field_whitelist | ["session_id", "user_input", "ai_output", "data_source", "timestamp"] | Only retains fields required for auditing, reduces log storage overhead |
api_response_log_threshold | 500 milliseconds | Logs spot/futures data calls that exceed the threshold to troubleshoot latency anomalies |
thread_pool_size | 4–6 | Reduces the probability of exceeding vectorization concurrency rate limits, adapts to single-batch data scale |
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 targeted analysis. It is recommended to test against your own samples before finalizing settings.
Three common configuration mistakes
- Phenomenon: The Human field is null in the API call response preview. Cause: The
userparameter was not passed correctly in the request body, or the parameter format does not comply with FastGPT interface specifications. - Phenomenon: Rate limit exceeded error triggered for vectorization tasks. Cause:
embedding_batch_sizeandthread_pool_sizeare set too high, exceeding platform rate limits. - Phenomenon: Unable to filter records exclusive to a specific enterprise in the knowledge base. Cause: The
record_owner_fieldconfiguration was not enabled, and no enterprise identifier field was bound to each knowledge base document.
How to confirm configuration is complete
- Log in to the FastGPT backend log module, filter by session ID, confirm that associated logs for each investment research conversation are bound to the same session identifier.
- Construct a test API request with the
userparameter, check if the Human field in the response preview correctly displays the request content. - Submit batch plastics and rubber data for vectorization, confirm that no rate limit exceeded error prompts appear during task execution.
- Check the metadata of knowledge base documents, confirm that each record is bound to a dedicated identifier field available for permission filtering.
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.