What the Data for This Category Looks Like
Data sources for construction and decoration investment research include public bidding announcements, building material supplier price ledgers, industry construction specification documents, and project completion archives. Update rhythm adjusts based on project milestones and building material market fluctuations, with no fixed uniform cycle.
Documents include structured tables (such as material specifications, unit prices, and supplier information) and unstructured PDFs (such as construction process guides and project acceptance reports). Fields include material model, environmental protection certification level, construction process duration, project budget amount. Some documents include units of measurement such as square meters and cubic meters.
Constraints Imposed on Multi-turn Dialogue and Prompt Configuration
The data characteristics of construction and decoration investment research impose multiple constraints on multi-turn dialogue and prompt configuration.
Numerous and detailed structured fields require multi-turn dialogue to retain precise context such as material models and project names. This prevents parameter mismatches in subsequent queries. A non-negligible share of unstructured documents requires prompts to include format adaptation guidance. This converts recall results from different document types into readable content. Since updates follow no fixed schedule, prompts must include timeliness verification logic. This prioritizes the most recently updated data in the knowledge base. Diverse units of measurement require automatic unit unification in multi-turn dialogue. This avoids confusion during investment research comparisons.
How to Set Configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Construction and decoration investment research data includes multiple material price lists and project ledgers. Multi-turn dialogue must retain core fields such as material models and project names, and this value range prevents context overflow |
Recall count | Top 8–12 results | Descriptions of building material models and construction processes are highly detailed. Too many recall results will cause context redundancy, while too few will miss key comparative information |
Similarity threshold | 0.75–0.85 | Parameter descriptions of similar building materials have high similarity. A threshold that is too low will recall irrelevant data, while a threshold that is too high will miss investment research comparison content for the same category of materials |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Large construction specification and completion archive documents have substantial content and long parsing times. This value range prevents file parsing failures |
Rerank result count | Top 4–6 results | The most matching building material quotations or construction solutions must be prioritized to reduce user screening costs |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material types, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: After multi-turn dialogue is triggered, the
global.workerPoll.countGptMesstatistic does not accumulate as expected, and only records a single interaction data point. Cause: The context transfer logic for multi-turn messages is not configured in advanced orchestration, causing the second message to not be included in session statistics. - Phenomenon: Markdown format tables recalled from the knowledge base only display raw code without rendering effects in external dialogue channels. Cause: No format adaptation rules for dialogue output are configured, and Markdown syntax is not converted to channel-compatible rich text format.
- Phenomenon: After executing an SQL query via a workflow, the query results cannot be synchronized to the AI dialogue window, and only placeholders are displayed in replies. Cause: No result binding node is configured in the workflow, and structured data returned by SQL is not mapped to context variables in AI replies.
How to Confirm Proper Configuration
- Navigate to the session log page, check whether the context window includes core fields from multi-turn dialogue, confirm no truncation occurs, and adjust configurations until requirements are met.
- Trigger a test dialogue with multiple rounds of interaction, verify whether the
global.workerPoll.countGptMesstatistic matches the actual number of interactions, and adjust the context configuration in advanced orchestration. - Send a query containing Markdown format content to the knowledge base, check whether the content renders normally in external dialogue channels, and adjust format adaptation rules until expectations are met.
- Run the workflow bound to the SQL query, confirm whether the query results are automatically filled into the corresponding positions of AI replies, and adjust the configuration of the result binding node in the workflow.
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.