What the Data for This Category Looks Like
Engineering consulting yield data comes from three sources: internal cost estimation ledgers, construction decoration cost reference indicators released by industry associations, and project investment plans provided by requesting entities. The data update rhythm adjusts based on project progression: every two weeks during the planning phase, weekly during the construction drawing phase, and real-time synchronization during the settlement phase.
Documents use a structured table format, including unique project identifier, consulting service scope, benchmark yield parameters, calculation basis document number, yield calculation values for each phase, and data verification mark. The following fields are included: project_id (string type), benchmark_return (ratio value, no unit), stage_return (ratio value, no unit), verify_flag (boolean type).
Constraints Imposed on Workflow Orchestration
Dispersed data sources mean workflows must include multiple data source pull nodes and implement data alignment. Phased update rhythms mean workflows must support triggering execution based on project phase, eliminating redundant execution from fixed scheduled triggers. Structured table documents mean workflows must include dedicated table parsing nodes to extract specified fields. Unique field requirements mean workflows must include field verification nodes to filter duplicate or invalid data. Parallel calculation scenarios for multiple roles mean global variables must support user-based isolation to prevent data conflicts.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
trigger_mode | Trigger bound to the phase associated with project_id | Adapts to the phased update rhythm of engineering consulting projects, avoiding redundant execution from fixed scheduled triggers |
data_source_pull_count | First 3 data sources | Covers the three core data source types: internal ledgers, industry indicators, and documents from requesting parties; prevents excessive data sources from causing execution timeouts |
field_extract_batch_size | 1200 characters | Matches the single-page table length of engineering consulting estimation documents, ensuring complete field extraction |
global_var_scope | Isolated by user_id | Adapts to parallel calculation scenarios for multiple consulting engineers, preventing interference between global variables of different users |
post_prompt_template | "Please combine the {stage_return} value from this calculation to supplement key cost adjustment points" | Matches the business guidance needs of engineering consulting scenarios, replacing the system default guidance |
workflow_timeout | 600 seconds | Covers the time required for multi-data source pulling and table parsing, preventing execution interruptions |
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 it is recommended to test on your own samples before finalizing.
Three Common Configuration Mistakes
- Phenomenon: Calling the workflow API returns the
context_invalidstatus code, and historical conversation context is missing from the response. Cause: The workflow’scontext_scopeconfiguration is not set to binduser_id. This means context is not isolated by user, leading to mutual overwriting of context across different requests. - Phenomenon: Workflow execution fails to load previously saved global variables, and global variable fields display as empty. Cause: The global variable loading trigger node is not configured to load when the session starts. Variables are only initialized in the current session, so historical data cannot be reused across sessions.
- Phenomenon: After the AI generates a reply, only the system default "guess you might want to ask" guidance text is displayed, and no customized business guidance content is generated. Cause: No customized guidance template is configured in the workflow’s
post_prompt_template, so the system default configuration is used.
How to Confirm Proper Configuration
- Send a request with a test
user_idandproject_id, then check if the returned results include correct context information. - Send two requests with the same
user_id, then check if the global variables of both requests remain consistent, and if global variables from requests with differentuser_idvalues do not interfere with each other. - Trigger workflow execution, then check if the configured customized guidance template content displays after the AI reply.
- View the workflow execution log, then confirm that the trigger mode, number of pulled data sources, and timeout settings match the configured values.
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.