What the Data for This Category Looks Like
Data for this category comes from production scheduling systems of heating operation enterprises, energy consumption metering terminals, and local public utility regulatory platforms. Data is aggregated daily for all stations from the previous day. Daily reports are finalized before 2 AM the next day. Real-time metering data for core stations refreshes every hour. Daily reports only retain daily aggregated results.
Each daily report uses a structured format. It includes fields such as unique station ID, total daily heat supply, unit heat supply cost, total operating cost, total revenue, number of households covered by regional heating, and more. Heat supply is measured in gigajoules. Cost and revenue are measured in yuan. Number of households is measured in individual households. Each document contains single-line data for dozens of stations. Total character count ranges from 5000 to 15000.
Constraints Imposed on Multi-turn Conversations and Prompt Engineering
The data characteristics of this category impose multiple constraints on multi-turn conversation and prompt engineering configurations. First, data sources include internal enterprise systems and regulatory platforms. Field standardization levels vary. Prompts must clearly specify standard names and formats for required fields. This prevents the model from confusing unique station IDs with other numbered fields. Second, daily report data is aggregated daily and has large per-document size. Context recall for multi-turn conversations must be limited. This prevents irrelevant historical daily report data from interfering with current analysis. Third, update cycles differ between real-time metering data and daily aggregated data. Prompts must clearly specify query time ranges. This prevents the model from mixing real-time same-day data with previous day’s aggregated results. Additionally, each single document contains dozens of fields. Prompts must guide the model to only extract specified fields. This avoids generating irrelevant information.
Configuration Settings
| Configuration Item | Suggested Value | Basis for This Setting |
|---|---|---|
maxContext | 6–10 | Multi-turn conversations for heating industry daily reports typically need to retain recent station queries and parameter requests. Excessive context increases model inference load. Insufficient context prevents association of station IDs from historical queries |
Retrieval Count | 3–5 | Each daily report contains structured data for dozens of stations. Excessive retrieved fragments lead to context redundancy. Insufficient fragments fail to cover all fields for required stations |
Similarity Threshold | 0.75–0.85 | Matching accuracy for station IDs and data fields is high. A threshold that is too low introduces data from irrelevant stations. A threshold that is too high may fail to retrieve correct station information |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Structured data in each daily report has large volume. Parsing requires sufficient time for field extraction and format conversion |
Segment Length | 800–1200 characters | Each station’s data has multiple fields. Segment length adapts to structured document splitting, avoiding field breakage that impairs model understanding |
Reordered Return Count | 2–3 | Multi-turn conversations only need to return data for 1-2 stations specified by the user. Excessive returns lead to information redundancy |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume and business rules. Analyze specific issues on a case-by-case basis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Issue: In the AI chat node of FastGPT 4.10.0, setting
maxContextto 30 context entries, but only 2 context entries are displayed in conversation details, and replies cannot associate historical context. Cause: The global context transfer switch for the workflow is not enabled, or the node's context configuration is not bound to the workflow's global context variable. - Issue: The AI chat node prompt in the workflow cannot recognize spaces in input text, leading to failed matching of station IDs or field names. Cause: The prompt does not explicitly require retaining spaces in input text, or the space retention rules are not configured in the text preprocessing link.
- Issue: Multi-turn conversations cannot reference the completion reason field value from a previous AI chat component, preventing subsequent nodes from executing branch judgments based on this result. Cause: The completion reason field is not configured as a referenceable output variable for the workflow, or subsequent nodes do not correctly bind the reference path of this variable.
How to Verify Correct Configuration
- Open the workflow configuration page, check the context configuration parameters of the AI chat node. Confirm that the values match the required number of historical conversations for the business.
- Upload a test heating industry daily report document. Run the workflow and view the retrieved document fragments. Confirm that the quantity matches the configured retrieval count requirement.
- Enter a query statement containing spaces and station IDs. Run the workflow and check the reply results. Confirm that the prompt correctly recognizes spaces and extracts corresponding fields.
- Configure a test case for loop verification. Run the workflow and confirm that branch logic and loop trigger conditions execute as expected.
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.