What the Data for This Category Looks Like
Industrial park investment research data includes three categories of sources: officially archived park planning indicator ledgers, industrial and commercial and operational data of settled enterprises, quarterly/annual industrial support policy documents, and supporting update data such as surrounding transportation and land transfer. Data update rhythms fall into three categories: operation-led ledgers are updated monthly, policy documents are synced immediately upon release, and planning data is calibrated quarterly. Document structures cover structured, semi-structured, and unstructured formats. Structured fields include park code, unified social credit code of settled enterprises, rental price (yuan/square meter·month), annual tax contribution (ten thousand yuan). Semi-structured documents are industrial support policy official documents and park investment promotion brochures. Unstructured content includes annual park operation review reports and settled enterprise interview minutes.
What Constraints Do These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
The multi-dimensional characteristics of industrial park investment research data impose clear constraints on multi-turn dialogue and prompt configuration. There are many structured fields with strict unit requirements, so prompts must clearly specify the standard expressions and units of fields to avoid model confusion over data calibers. Update frequencies of multi-source data vary widely, so prompts must limit the data source time range for queries to prevent calling expired information. Unstructured documents have long lengths, so multi-turn dialogue must limit the context window size to avoid exceeding the model's input limit. Queries often involve multiple park and enterprise entities, so multi-turn dialogue must track context entity identifiers to prevent mixing up query objects across turns.
How to Configure the Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxInputToken | 10000–14000 token | Industrial park investment research queries often include multiple park names, settled enterprise information, and historical conversation context, requiring sufficient input length to carry complete questions |
recallTopK | Top 8 entries | Industrial park data is mostly structured in chunks; excessive recall will cause context overload and affect model inference efficiency |
similarityThreshold | 0.75 | Precise matching of exact fields such as park codes and enterprise IDs is required to avoid recalling irrelevant park or enterprise data |
promptPrefix | Custom template including "Please base your responses on data sources such as park operation ledgers and policy documents, strictly follow field units, and track the current query's park and enterprise entities" | Matches the data source requirements and context tracking needs of industrial park investment research |
enableHistorySummary | Enabled, generate a summary every 3 turns | Multi-turn dialogue involves multiple parks and data points; summaries compress context to avoid exceeding model window limits |
maxTokenPerRequest | 16000 token | Unstructured operation reports for industrial parks have long content, requiring sufficient generation tokens to fully output analysis results |
The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to perform tests on relevant samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: When a user inputs a query containing multiple sets of park data, a
413 Request Entity Too Largeerror is triggered, or a no-result prompt is returned. Cause: ThemaxInputTokenparameter is not configured, and no interception is set for user input length, resulting in exceeding the model's input limit. - Phenomenon: The
citationsfield is empty in results returned by dialogue interface calls. Cause: The citation marking function for knowledge base recall is not enabled, or the prompt does not require the model to bind data source citations, making it impossible to generate citation identifiers. - Phenomenon: In multi-turn dialogue, when a user follows up to query the same indicator for another park, the model incorrectly associates the context with the previous round's park. Cause:
enableHistorySummaryis not enabled, or the prompt does not clearly require tracking the current query's entity identifiers, leading to context confusion.
How to Verify Proper Configuration
- Submit test input containing multiple sets of park data queries, verify whether the system intercepts over-limit inputs according to the configured
maxInputTokenlimit and returns a corresponding prompt. - Initiate queries with clear data source requirements, check whether the
citationsfield is included in results returned by dialogue interface calls, and confirm that the model has bound the correct knowledge base source. - Initiate two consecutive queries related to different parks, verify whether the model can correctly distinguish context and return accurate data for the current query's park.
- Enable thinking process configuration, view conversation history to confirm that the thinking process is fully recorded and displayed.
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.