Deployment and Upgrade of Thermal Industry Investment Research Knowledge Base Construction

Thermal industry investment research data comes from multiple sources. These include pipe network operation time-series data collected via thermal

Data Characteristics for This Category

Thermal industry investment research data comes from multiple sources. These include pipe network operation time-series data collected via thermal enterprise SCADA systems, environmental data from meteorological monitoring stations, industry regulatory policy documents, quarterly financial reports and operation and maintenance logs of listed thermal enterprises. Data update frequencies vary. Pipe network operation parameters update every minute or second. Policies and financial reports release quarterly or on an ad-hoc basis. Operation and maintenance logs generate alongside events. Single structured data entry includes fields such as 采集时间, 热力站ID, 供水温度, 回水压力, 供热量 and other fields. Units are seconds, none, ℃, MPa, GJ respectively. Unstructured research reports and logs use plain text or PDF formats. They contain long-form operation and maintenance analysis content.

Constraints for Deployment and Upgrade

High-frequency time-series data updates require deployments to use data source connectors that support batch real-time synchronization. This avoids single-entry data processing delays. Structured data has high field standardization but large total volume. Pre-set field mapping rules during deployment to prevent field mismatches during subsequent imports. Unstructured operation and maintenance logs and research reports have long-text and bulk upload requirements. Adjust file parsing timeout and segmentation parameters to adapt to long-document processing. During upgrades, maintain compatibility with previously collected time-series data formats to prevent existing data sources from failing after version updates. Also, synchronously update field mapping templates to accommodate newly added regulatory data fields.

Configuration Parameters

Configuration ParameterRecommended ValueRationale
UPLOAD_FILE_MAX_SIZE2000 MBIndividual thermal operation and maintenance logs or research report PDFs may exceed standard document sizes. This setting adapts to bulk upload requirements
PARSE_FILE_TIMEOUT_SECONDS600 secondsLong time-series data parsing or bulk PDF parsing takes extended time. This prevents mid-process timeout interruptions
chunkSize800–1200 charactersThermal research reports and operation and maintenance logs contain long paragraph content. This segmentation length adapts to text semantic integrity
similarityThreshold0.75–0.85Filters low-match similarity search results for unstructured logs and structured parameters
recallTopKTop 10 entriesThermal industry investment research requires coverage of multi-station operation parameters. This retrieves sufficient associated data
SYNC_DATA_BATCH_SIZE500 entries per callAdapts to bulk synchronization of high-frequency collected time-series data. This prevents service lag caused by overly large single synchronization volumes

The parameter values provided on this page are general recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Each scenario requires individual analysis. It is recommended to test on your own samples before finalizing settings.

Three Common Misconfigurations

  • Symptom: Container startup returns MONGODB CONNECTION FAILED error code 500. Knowledge base index cannot be viewed. Cause: Database connection parameters are not correctly configured in docker-compose.yml, or database indexes are not initialized.
  • Symptom: After bulk importing thermal time-series data, some fields are empty. Cause: Field mapping rules are not pre-set. This causes automatic field matching to fail during import. Core fields such as 采集时间 and 供热量 are not bound to knowledge base fields.
  • Symptom: After upgrading to version 4.9, original third-party proxy configuration no longer takes effect. Cause: Proxy configuration is not updated to aiproxy. Proxy service address and port in environment variables are not adjusted.

How to Verify Proper Configuration

  • Log in to the FastGPT backend, navigate to the data source configuration page, and test the connection to the thermal enterprise SCADA system connector. Confirm that normal collected data is returned.
  • Upload a thermal operation and maintenance log PDF, view the parsed segmentation results. Confirm that the segmentation length matches the preset chunkSize value.
  • Execute a bulk synchronization task, check the synchronization count in the task log. Confirm that it matches the SYNC_DATA_BATCH_SIZE setting. No field mismatch errors appear.
  • Check environment variable configurations. Confirm that proxy service parameters for version 4.9 have been replaced with aiproxy related configurations. No leftover legacy proxy configuration items remain.

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.