What the Data for This Category Looks Like
The marketing content and operational data for the heating industry primarily comes from heating company SCADA monitoring systems, user heating payment ledgers, offline marketing activity ledgers, and online campaign backend platforms. Data update cadence falls into two categories: operational data is updated hourly, and marketing outreach data is updated daily. The standard structure of a single document includes fields such as heating station code, peak heat supply, user heating time period proportion, marketing activity name, outreach channel, and converted user count. The unit for heat supply is gigajoules (GJ), the unit for user count is households, and the timestamp format is YYYY-MM-DD HH:MM:SS.
Constraints Imposed on Knowledge Base Retrieval and Recall
Three core constraints are imposed on the retrieval and recall link by the data characteristics of the heating industry. First, hourly updated operational data requires the retrieval pipeline to support real-time data recall within the last 24 hours. A short-cycle knowledge base refresh mechanism must be configured to avoid using expired heat supply data that could negatively impact marketing decisions. Second, multi-field structured data requires retrieval to support multi-dimensional combined matching. Filtering recall results by fields such as heating station code and time range must be enabled to ensure content accurately aligns with the marketing needs of the target heating station. Third, strict requirements for field units mandate unifying data formats before retrieval, to prevent recall errors caused by mismatched units.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall count | top 8-12 results | Single pieces of heating industry marketing content have high information density. Too many recall results will exceed the context window limit, while too few will fail to cover sufficient business-related information |
similarity threshold | 0.75-0.85 | Heating industry data includes multiple sets of structured business fields. A threshold that is too low will introduce operational data from unrelated stations, while a threshold that is too high will miss marketing content that meets the required matching degree |
chunk length | 800-1200 characters | Heating industry operational documents contain combined content of numerical indicators and business descriptions. Segments that are too long will destroy semantic connections, while segments that are too short will disrupt business logic |
knowledge base tag filtering | bind tags by heating station code | Marketing content must accurately match the heating data of the corresponding station. Binding tags by station code filters out recall results from unrelated stations |
maxContext | 3000-4000 characters | Heating industry marketing retrieval requires combining historical operational data and current campaign content. A sufficiently large context window can fully carry multiple sets of related documents |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Batch-imported heating industry operational data files usually contain historical data from multiple stations. Sufficient parsing time must be reserved to complete field extraction and vectorization |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis, and testing on internal samples prior to finalization is recommended.
Three Common Misconfigurations
- A prompt reading "No available channel found for model gpt-4o-mini under current group default" is returned when calling knowledge base retrieval. The cause is that the API channel for the corresponding model is not bound to the current group, or the channel configuration is not associated with the exclusive knowledge base group for heating industry marketing content.
- When using variable references to select knowledge base documents, the retrieval result is empty or matches incorrectly. The cause is that the field parameters bound to the variable are not correctly configured, and the heating station code is not specified as the core matching dimension.
- After configuring a custom OpenAPI Token, knowledge base retrieval still incurs platform fees. The cause is that the custom Token is not bound to the corresponding knowledge base retrieval service, and the platform's default billing channel is still being used.
How to Verify Proper Configuration
- Upload a single heating industry operational document to trigger the knowledge base parsing process. Check whether the parsed field list includes core business fields such as heating station code and peak heat supply, and whether the field units match the preset standards.
- Enter a simulated query such as "XX heating station October heat supply optimization marketing plan" to confirm that the source documents of the recall results include the station's code, and that the similarity score falls within the set threshold range.
- Configure the tag filtering rule for heating station codes, select the tag for a specified station, and initiate a retrieval. Confirm that the returned results only include marketing content related to that station.
- Call the knowledge base retrieval API, pass in the custom OpenAPI Token and specify the target knowledge base group. Check whether the billing field in the request response displays the account information associated with the custom Token.
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.