Dialogue Logging and Auditing for Air Pollution Control Investment Research Knowledge Base Construction

Air pollution control investment research data mainly comes from real-time collected data from environmental monitoring stations, air pollution model

What the data for this category looks like

Air pollution control investment research data mainly comes from real-time collected data from environmental monitoring stations, air pollution model simulation results, industry emission enterprise ledgers, environmental protection policy and process standard documents. Real-time monitoring data updates hourly. Simulation and ledger data update with project progress. Policy documents are revised quarterly or annually.

Each single document contains fields such as point coordinates, pollutant types (PM2.5, NOx, SO₂, etc.), concentration values (units μg/m³ or mg/m³), emission limits, governance process parameters, and more. Some long documents exceed 10,000 words and need to be split by chapter for knowledge base retrieval.

Real-time hourly data requires dialogue logs to accurately bind timestamps. This avoids confusing and associating monitoring data from different time periods.

Multi-field and multi-unit characteristics require the auditing link to verify field completeness and unit consistency. This prevents errors caused by mixing concentration value units.

The combination of long documents and multi-source data requires logs to record data source identifiers and retrieved fragment positions. This makes it easy to trace knowledge base call logic.

Data with different update frequencies needs to be marked with time validity tags in logs. This distinguishes call scenarios between real-time monitoring data and static policy documents.

How to configure the settings

Configuration ItemRecommended SettingRationale
LOG_SAVE_INTERVAL10 secondsAdapts to the time-series association requirements of air pollution control hourly monitoring data, prevents batch loss of logs under high-frequency data
MAX_LOG_RETENTION_DAYS365 daysMeets general audit retention cycle requirements for the environmental protection industry, covers complete quarterly and annual investment research cycles
HISTORY_QUERY_FILTER_FIELDScustomUid, dataSourceTagSupports filtering conversation history by user identifier and data source (monitoring station / simulation model), adapts to multi-source data auditing scenarios
API_CANCEL_ON_DISCONNECTEnabledTerminates unfinished requests when the client disconnects the SSE connection, avoids generating invalid unfinished log entries
PARSE_FIELD_VALIDATION_SWITCHEnabledRecords the verification results of air pollution control data fields called during conversations, facilitates troubleshooting parameter matching errors during audits

The parameter values provided on this page are conventional 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. It is recommended to test on your own samples before finalizing settings.

Three common mistakes

  • Phenomenon: After the client disconnects the SSE connection, the dialogue log is not written to the database, and a 408 Request Timeout status code is returned. Cause: The API_CANCEL_ON_DISCONNECT configuration is not enabled. The request still occupies resources after disconnection, and the log persistence logic is not triggered.
  • Phenomenon: The 3000 port cannot be accessed after the container starts, and the log prompts MongoDB connection failed: authentication failed. Cause: The MONGO_INITDB_ROOT_USERNAME and MONGO_INITDB_ROOT_PASSWORD parameters are not configured correctly, or the database version is incompatible with the FastGPT image version.
  • Phenomenon: Calling the conversation history API returns full session data, and filtering by customUid is not possible. Cause: HISTORY_QUERY_FILTER_FIELDS is not configured as customUid, or the customUid parameter is not carried in the API request.

How to confirm the configuration is correct

  • Initiate a conversation request carrying a specified customUid, call the conversation history API, and confirm that only session records associated with that customUid are returned.
  • Manually disconnect the SSE connection, check the database log, and confirm that no invalid unfinished dialogue entries are generated.
  • Check the container startup log, and confirm that the MongoDB connection successful prompt appears normally.
  • Trigger a conversation containing air pollution control data fields, check the audit log, and confirm that the field verification results are fully recorded.

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.