What the data for this category looks like
This data provides core support for water treatment industry analysis in financial investment research scenarios. Sources include water quality monitoring bulletins from ecological environment departments, process manuals from water treatment equipment manufacturers, real-time collection logs from online monitoring equipment, and industry standard documents.
Update cycles vary significantly: real-time monitoring data updates hourly or by the minute, industry standard documents update annually or quarterly, and project acceptance reports are archived per project cycle.
Document fields include monitoring point latitude and longitude, water quality indicators (COD, ammonia nitrogen, total phosphorus, etc., unit mg/L), process parameters (flow rate m³/h, pressure kPa), standard limits, equipment operation logs, and more. There are both short-text indicator data and long-text process description documents.
How These Characteristics Affect Citation Source and Traceability
Data sources are scattered, and update cycles differ greatly. During traceability, it is necessary to clearly distinguish credibility markers between real-time collected data and historical documents, to align with compliance verification requirements in investment research scenarios.
Fields include multiple types of units and precise numerical values. When citing, original units and numerical precision must be retained to avoid altering data and affecting the accuracy of investment research conclusions.
Long-text process documents and short-text monitoring data are mixed. It is necessary to adjust segmentation and recall logic based on document types to ensure contextual integrity of cited content.
Some data is associated with specific points and times. During traceability, point numbers and collection times must be attached to meet data traceability requirements in financial investment research.
How to Set the Configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
recall count | Top 8-12 entries | Water treatment data mostly consists of precise parameters for niche scenarios. Excessive recall will introduce irrelevant content, while insufficient recall will fail to cover all valid information |
similarity threshold | 0.75-0.85 | Water treatment indicators have clear numerical ranges. A threshold that is too low will introduce irrelevant monitoring data, while a threshold that is too high will miss valid process parameters |
segment length | 800-1200 characters | Water treatment process documents mostly contain continuous process descriptions. Segments that are too long will lose contextual connections, while segments that are too short will disrupt process logic |
citation source display fields | Monitoring point number + data collection time + original document title | Water treatment data requires clear traceability to specific collection points and times to meet compliance traceability requirements in investment research scenarios |
embedding model | text-embedding-3-large | Numerical features of water treatment indicators require high-dimensional embedding to accurately match similar scenarios, adapting to parameter matching needs in niche industries |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large water treatment process manuals takes a long time. This avoids interrupting the parsing process due to timeout |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing the settings.
Three Common Mistakes
- Phenomenon: Service lag occurs when calling
text-embedding-3-large. Restarting the service after commenting out the model does not resolve the issue. Cause: No cache strategy for embedding vectors is configured, leading to repeated calculation of high-dimensional features, occupying a large amount of system resources and affecting the response speed of investment research queries. - Phenomenon: Returned answers do not strictly match the original question-answer pairs in the knowledge base, and freely generated supplementary content appears. Cause: The configuration switch for forced citation of original text is not enabled, or the recall count is set too low, failing to cover all preset investment research question-answer pairs, leading to the generation of irrelevant content.
- Phenomenon: After querying data from the database in the workflow, the
sourcefield of the returned result is empty, and no citation source is displayed. Cause: The configuration for returning citation sources is not enabled in the workflow node, or the citation source display fields are not mapped correctly, failing to meet the traceability requirements for investment research reports.
How to Confirm the Configuration Is Correct
- Upload a single water treatment monitoring data file, confirm that the parsed segments retain original fields and units, meeting the accuracy requirements of investment research data.
- Initiate an investment research query containing specific water quality indicators, confirm that the citation sources in the returned results include collection points, timestamps and other identifiers, which can be used to verify data compliance.
- Check service logs, confirm that there are no abnormal timeout records for
embedding modelcalls, ensuring the stability of investment research queries. - Trigger a preset investment research question-answer pair query, confirm that the returned content is completely consistent with the original text in the knowledge base, avoiding the generation of irrelevant supplementary content.
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.