Model Integration and Configuration for Thermal Industry

What this category of data looks like

Thermal industry investment research data originates from four main sources: thermal production scheduling systems, pipe network monitoring platforms, meteorological data APIs, and enterprise operational reports. The data falls into three categories:

Minute-level pipe network monitoring point time-series data
Daily operational statistical reports
Monthly supply and demand analysis reports

Time-series data fields include monitoring point ID, collection time, water supply temperature, return water pressure, and flow rate. Their corresponding units are: none, ISO 8601 timestamp, ℃, MPa, m³/h. Reports and analysis documents are long files combining structured tables and paragraphs. Update frequencies vary significantly: time-series data updates every minute, reports update daily, and analysis reports update monthly.

What constraints these characteristics impose on model integration and configuration

The multiple data types, varied high update frequencies, and dedicated field units of thermal industry investment research data create multiple constraints for the model integration process. First, the system must support mixed import of time-series data and unstructured documents, and adapt synchronization scheduling logic for different update frequencies. Second, field mapping rules must be configured to ensure the model can accurately identify and use thermal data with dedicated units, and prevent unit conversion errors. Third, adjustments to context capacity parameters are required for long document analysis, to avoid truncation of key thermal supply and demand data. The system must also adapt to the time requirements of batch data import to prevent parsing timeouts.

How to set configurations

Configuration Item	Recommended Value	Rationale
`maxContext`	`8000–12000 characters`	Thermal monthly analysis reports typically range from 5000 to 10000 characters, so this range ensures full context retention for single long documents
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Batch thermal monitoring CSV files have large data volumes and require longer parsing time
`recall count`	`Top 8–12 entries`	Thermal industry investment research requires coverage of time-series data from multiple monitoring points. Too many recalled entries will exceed context limits, while too few will lead to loss of critical information
`similarity threshold`	`0.75–0.85`	Thermal data fields have a high degree of standardization. This range filters low-match irrelevant monitoring data to avoid analysis interference
`UPLOAD_FILE_MAX_SIZE`	`1000 MB`	Annual operational report packages from thermal enterprises are typically large, so this setting supports large file uploads
`BATCH_SYNC_INTERVAL`	`60 seconds`	High-frequency time-series data requires regular synchronization to ensure real-time performance of investment research data

The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis. Testing should be conducted on local samples before finalizing configuration settings.

Three common configuration mistakes

Phenomenon: After deploying the model service in an internal network, logs prompt connection failure and the service restarts continuously. Cause: The OPENAI_BASE_URL environment variable is not fully configured, or does not point to an internal network-accessible model service address.
Phenomenon: After configuring an English prompt, the model only outputs Chinese content. Cause: The model configuration does not enforce output language, and the knowledge base has an excessively high proportion of Chinese thermal data, which dominates the model generation logic.
Phenomenon: Parsing times out and fails during batch import of thermal monitoring data. Cause: The PARSE_FILE_TIMEOUT_SECONDS configuration value is too low, and does not adapt to the parsing time required for batch data.

How to confirm correct configuration

Import a single thermal monthly analysis report, verify that the parsed text in the knowledge base is complete without truncation or garbled characters, and confirm that the configuration adapts to the document length.
Initiate a query related to thermal industry investment research, check that the number of recalled knowledge base entries matches the configured recall count, and verify the filtering effect of the similarity threshold.
Call the model service in the internal network environment, check that the returned results meet the configured language requirements, and confirm the correctness of the OPENAI_BASE_URL configuration.
Import batch thermal monitoring CSV files, verify that the parsing time falls within the range specified by PARSE_FILE_TIMEOUT_SECONDS, with no timeout errors.

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.