What the Data for This Category Looks Like
Data sources for water industry investment research include pipeline monitoring logs from water utility operation enterprises, water quality test reports, feasibility study reports for municipal water projects, water quality bulletins from ecological environment departments, and annual survey materials from industry associations. Data update rhythms cover multiple dimensions: real-time pipeline monitoring data is updated hourly, monthly operation reports are updated monthly, and policy documents and industry survey materials are released irregularly. Document structures mostly include structured tables (with fields such as monitoring point ID, collection time, indicator value), long-text feasibility study reports, and policy clauses. Core indicator units include mg/L (water pollutant concentration), kPa (pipeline pressure), and 10,000 tons/day (water supply scale).
Constraints on Multiturn Dialogue and Prompting
The multi-dimensional update rhythm of water industry investment research data requires multiturn dialogue contexts to filter relevant content based on data timeliness, to avoid introducing expired real-time monitoring data. The high proportion of structured table documents requires prompts to clearly specify field extraction rules, to ensure returned content matches standard units and classification logic of water industry indicators. The existence of long-text feasibility study reports and policy documents requires limiting the total length of single-turn retrieved context, to prevent exceeding model token limits. Different data types with varying update frequencies need distinct calling rules in prompts, to ensure real-time queries match hourly monitoring data, and trend analysis matches monthly or annual reports.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Water industry documents include long-text feasibility study reports and multiple structured tables. This range retains sufficient context while avoiding token overflow |
Recall count | Top 6–8 entries | Water industry investment research data mostly consists of multi-point monitoring data. This range covers sufficient monitoring point information without redundancy |
Similarity threshold | 0.72–0.80 | Water industry indicator names (such as COD, ammonia nitrogen) often have similar expressions. This range balances retrieval precision and coverage |
Chunk size | 1000–1500 characters | This range adapts to the paragraph length of water industry structured tables and long-text reports, avoiding splitting that breaks indicator associations |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Large water industry feasibility study reports contain substantial content, requiring sufficient parsing time |
Rerank result count | Top 3–5 entries | This prioritizes returning the most relevant core monitoring data or policy clauses, reducing context load for multiturn dialogue |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis, and it is recommended to test against your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: A "No available indexing model detected" error appears after refreshing the knowledge base page. Cause: Structured documents for water industry investment research (such as water quality reports) are not correctly bound to the specified vector model index, or the indexing model deployment status fails to sync after page refresh.
- Symptom: When calling historical water industry monitoring data during multiturn dialogue, returned results do not match the current query. Cause: The prompt fails to clearly specify filtering context by data collection time, leading the model to call expired monthly report data.
- Symptom: Customer service dialogue response time exceeds 10 seconds. Cause: Retrieval count is set too high, and segmented retrieval optimization is not enabled, causing each dialogue to load a large amount of context content from water pipeline monitoring data, increasing model inference time.
How to Verify Configuration Correctness
- A new test dialogue is initiated, a query containing specific water industry monitoring points and indicators is entered, and returned content is checked to confirm inclusion of standard units and collection time, verifying that prompt rules take effect.
- A water industry monthly operation report is uploaded to trigger knowledge base parsing, and parsed segmented content is checked to confirm alignment with the set
Chunk sizeparameter range, verifying that parsing configuration takes effect. - The knowledge base index management page is viewed, the bound vector model is confirmed to be in running status, and the error prompt is checked to confirm disappearance, verifying that index configuration takes effect.
- Multiple consecutive queries are initiated, water quality data from different monitoring points is requested in sequence, and each response time is checked to confirm alignment with expected values, verifying the optimization effect of retrieval and reranking configurations.
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.