What the data for this category looks like
Industrial park research report data primarily comes from official announcements of park management committees, annual disclosure documents of operating entities, and public materials from third-party industrial real estate research institutions. Updates follow a fixed quarterly cycle, with temporary supplements for major park investment promotion or industrial upgrading events. Document structures typically include five standard modules: basic park overview, settled enterprise list, industrial cluster layout, rental prices, and supporting policies. Standardized metrics include rental unit price (unit: yuan/square meter·month), floor area (unit: square meters), number of settled enterprises (unit: enterprises), and other standardized indicators.
Constraints on multi-turn dialogue and prompt engineering
The decentralized sources and non-fixed update cycle of industrial park research reports require active confirmation of the data time range and recency with users during multi-turn dialogue, to avoid calling outdated information. The large number of document modules and long single-document content necessitate limiting the range of recalled document modules in prompts to reduce interference from irrelevant content. Fields have clear units, so uniform unit verification rules must be applied during multi-turn dialogue to prevent confusion between area and rental unit metrics. Temporary supplementary park updates may not be synchronized to the knowledge base in time, so prompts should guide users to indicate whether they need to supplement unincluded latest information.
Configuration settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Industrial park research reports have long single-document content, and multi-turn dialogue requires retaining conversational context across turns to avoid truncating dialogue history prematurely |
recall count | Top 6–8 results | Industrial park research reports have many subdivided modules; an appropriate number of recalls can cover information from different modules while avoiding excessive redundancy |
similarity threshold | 0.72–0.80 | High matching accuracy is required between fields in industrial park research reports and user queries; a threshold that is too low will introduce irrelevant content, while a threshold that is too high may fail to retrieve valid information |
chunk length | 1000–1500 characters | Industrial park research reports have clear paragraph structures; chunk length adapts to complete extraction of single-module content and avoids splitting that disrupts business logic |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Single industrial park research report files have large volume, requiring longer parsing time to avoid timeout interruptions |
clear_think_tag | Enabled | Prevents model-generated thinking tags wrapped in [THINK] from appearing in final responses during multi-turn dialogue, maintaining clean output |
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. Specific cases require individual analysis, and it is recommended to test against relevant samples before finalizing settings.
Three common misconfigurations
- Issue: Conversation responses retain model thinking content wrapped in
[THINK]tags without cleaning as expected. Cause: The code node for clearing thinking tags is not bound to the output link of the dialogue node, or the code logic does not correctly match the tag format. - Issue: Retrieved industrial park research report content from the knowledge base is incomplete, with key fields such as rental unit price data truncated. Cause: The
chunk lengthparameter value is too small, or themaxContextparameter does not adapt to the context requirements of multi-turn dialogue. - Issue: The model repeatedly asks irrelevant park policy details during multi-turn dialogue and fails to return responses focused on industrial layout queries. Cause: The prompt does not clearly limit the range of recalled document modules, causing the knowledge base retrieval logic to cover non-target content.
How to verify proper configuration
- Upload a complete industrial park research report, trigger a multi-turn dialogue, and check for uncleaned thinking tags in responses. If present, adjust the
clear_think_tagconfiguration or code node logic. - Initiate a conversation with multiple follow-up questions, verify that the model correctly associates the previous round's question with the current research report content. If context is lost, adjust the
maxContextparameter value. - Initiate queries covering different modules, such as rental prices and settled enterprises, confirm that retrieved content matches query requirements. If matching accuracy is too low, adjust the similarity threshold; if too much content is retrieved, adjust the recall count.
- Check workflow operation logs for file parsing timeout errors. If present, adjust the
PARSE_FILE_TIMEOUT_SECONDSparameter value.
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.