What This Category of Data Looks Like
Solid waste treatment yield and market data comes primarily from on-site project weighing systems, financial accounting modules, and regional renewable resource trading platforms. There are two data update schedules. Full daily report data completes the previous day's collection at 02:00 daily. Online monitored disposal efficiency data updates every hour. Data files use structured CSV or JSON format. They include fields such as project unique identifier, statistical cycle, total disposal weight, classified disposal proportion value, unit disposal cost, unit resource recovery income, and unit net income. Units are uniformly tons, yuan/ton, and yuan.
Constraints for Conversation Logs and Auditing
The multi-dimensional fields and scheduled update characteristics of solid waste treatment data create clear constraints for conversation logs and auditing. First, logs must fully record field filter conditions for every query. These include project identifiers, classified proportion thresholds, and more. This ensures that audit verifications can match query logic to raw data. Second, full daily data that updates at a fixed daily time requires logs to bind query timestamps. This prevents audit deviations caused by calling unupdated old data. Third, real-time monitored disposal efficiency data must mark the data source's update time in logs. This ensures that market data used in multi-turn conversations can be traced for timeliness. Finally, project unique identifiers must bind to conversation logs. This prevents audit confusion across projects.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | First 8 conversation records | Solid waste treatment yield queries require associating project historical filter conditions. 8 records cover the context needs of multi-turn conversations, and avoid model call timeouts caused by overly long context |
LOG_RETENTION_DAYS | 180 days | Meets industry compliance audit retention cycle requirements, and covers complete quarterly and semi-annual audit needs |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Solid waste treatment daily report data usually includes multi-project summary content, which takes longer to parse. 600 seconds covers parsing scenarios for most large files |
WORKFLOW_NODE_TIMEOUT | 120 seconds | Text content extraction nodes must process historical records from multi-turn conversations. 120 seconds avoids timeout failures caused by large data volumes |
RECALL_CHAT_HISTORY_COUNT | First 6 records | Balances context completeness and call efficiency, combined with general configurations and multi-turn query requirements for solid waste scenarios |
ENABLE_AUDIT_LOG | Enabled | Meets compliance audit requirements, fully records all conversation and workflow execution links, and facilitates subsequent traceability |
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 specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Symptom: After configuring a "Text Content Extraction" node in a workflow, multi-turn conversations cannot retain historical query conditions. Cause: Chat history parameters are not bound to the workflow's context delivery link. Only the recall count is configured within the node, and not synchronized to workflow input parameters.
- Symptom: In FastGPT v4.8.14, when the "Code Run" node input includes custom historical record fields, parameter verification fails and the workflow cannot be saved. Cause: This version imposes strict verification on input fields for workflow nodes. Undeclared custom fields are blocked.
- Symptom: When calling a deployed large model, occasional
llm model response emptyerrors occur, and requests hang for 10 seconds before terminating. Cause: No timeout retry mechanism is configured for the large model call link. Structured data parsing in solid waste scenarios consumes a large number of tokens, exceeding the token limit for a single call and resulting in an empty response.
How to Confirm Proper Configuration
- FastGPT’s audit log management page is accessed, conversation records for the corresponding project are filtered, each log is verified to include query parameters, call time, and return data timestamp, and the retention period is confirmed to match the configured
LOG_RETENTION_DAYSvalue. - A solid waste yield query that includes multi-turn filter conditions is triggered, workflow execution logs are viewed, and the "Text Content Extraction" node is confirmed to correctly recall the specified number of chat history records.
- A solid waste treatment daily report file that includes multi-project data is uploaded, the actual file parsing time is verified to not exceed the configured
PARSE_FILE_TIMEOUT_SECONDSthreshold, and timeout errors are avoided. - The "Code Run" node is tested with custom historical record field input, the workflow is confirmed to be saved and executed normally, and no parameter verification failure prompts are displayed.
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.