What This Category of Data Looks Like
City gas ledger data originates from station SCADA systems, on-site inspection terminal upload logs, and user service ticket systems. Three update rhythms apply:
- Pipeline operating pressure data is synced every 15 minutes
- Daily inspection ledgers are collected once per day
- Monthly summary reports are generated before the 5th of each month
Each ledger document is a structured table with these fields: pipeline point code, detection time, medium pressure, leak alarm level, inspector ID, and user address. Corresponding units are: 16-character strings, YYYY-MM-DD HH:MM, kilopascals, level 1-5, employee ID, and detailed address.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
The multi-source, heterogeneous update rhythms and structured field characteristics of city gas ledgers impose three constraints on multi-turn dialogue configuration:
- Differences in update frequencies across data sources require labeling the time attribute of data in conversation context. This prevents confusion between real-time inspection data and monthly summary reports.
- Fields include specific units and encoding rules. Prompts must explicitly require output matching field units. For example, pressure values must be labeled with kilopascals, and point codes must follow the 16-character string format.
- Ledgers are linked to pipeline nodes and user information. Multi-turn dialogue must retain previously queried point IDs or user numbers. This ensures context consistency for subsequent queries and avoids invalid results across unrelated objects.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind to the exclusive session ID of the corresponding gas station | Ensures multi-turn dialogue context only links to the current business scenario, preventing cross-project data confusion |
messages | Retain the most recent 20 historical conversation entries | Covers the full query chain within a single inspection cycle, while controlling total context length |
variables | Predefine two global variables: pipeline point code and statistical cycle | Enables quick access to relevant business data and reduces repetitive input steps |
| Maximum context length | 12000 token | Matches the total data volume of single-batch city gas ledger queries, preventing truncation of key field information |
| Context cleanup threshold | 8000–10000 token | Calibrated via real-world testing, prevents context overflow |
| Response timeout setting | 600 seconds | Calibrated via real-world testing, prevents long queries from being forcibly interrupted |
| Question optimization | Enable automatic simplification of user questions | Filters redundant phrasing, focuses on ledger fields and time ranges, and improves response accuracy |
Three Common Misconfigurations
- Scenario: Subsequent questions in multi-turn dialogue fail to link to previously queried pipeline points, and returned results match incorrect user addresses. Cause: Exclusive
chatIdis not bound, leading to session context reset or cross-scenario interference. - Scenario: Conversation logs show the large language model's thought process, but this content is not hidden on the front-end page. Cause: The
messagesparameter is not configured to filter thought process fields, or proxy settings do not correctly block intermediate output steps. - Scenario: Empty results are returned after submitting a ledger query request. Cause: Business parameters are not predefined via
variables, preventing the large language model from matching correct pipeline point codes or statistical cycle fields.
How to Verify Proper Configuration
- Launch a multi-turn dialogue that includes historical questions. Confirm that subsequent replies automatically link to previously mentioned pipeline points, and check that results match relevant business data.
- Review conversation records. Confirm that only user questions and the large language model's final replies are displayed, with no intermediate thought process content.
- Submit a query using business parameters that are not predefined. Confirm that the system prompts for necessary point or cycle information, verifying that the
variablespredefined logic works correctly. - Adjust conversation length. Confirm that old content is automatically cleared when historical conversations exceed the set retention limit, and no context overflow error is triggered.
The values listed here are common starting points. Adjust them based on specific samples and use cases.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.