What Data Looks Like for This Category
Air pollution control data used in financial investment research scenarios mainly comes from real-time time-series data from environmental monitoring stations, air pollutant emission inventories, national and local emission standard documents, project feasibility study reports, and engineering cases. Real-time monitoring data updates every minute, emission standard documents update annually or with revisions, and project documents iterate alongside project progress.
Document structures fall into three categories: structured monitoring data tables containing fields such as monitoring location, timestamp, and pollutant concentration; semi-structured standard documents organized by clause hierarchy; and unstructured engineering reports with extensive technical descriptions and parameter notes. Standardized metrics and units include pollutant concentration (μg/m³), emission rate (kg/h), wind speed (m/s), and similar items.
Constraints These Characteristics Impose on Knowledge Base Retrieval and Recall
High-frequency updates of real-time time-series data in financial investment research scenarios require the knowledge base to support incremental synchronization, avoiding resource consumption from full retransmissions. The multi-field attributes of structured monitoring data require support for filtering by dimensions such as location, time, and pollutant type during retrieval; otherwise, irrelevant data may be recalled, affecting investment research judgments.
Differences in long and short text between emission standards and engineering reports require chunking strategies adapted to different document lengths. Too short chunks may split technical logic, while too long chunks reduce semantic matching accuracy. Unit differences across multiple data sources require unified measurement standards in the preprocessing stage; otherwise, semantic deviations may occur during retrieval.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Covers the text length of short monitoring entries and long standard clauses in the air pollution control field, avoiding semantic fragmentation or reduced matching accuracy |
recall_top_k | Top 10–15 results | Balances recall needs for multi-location monitoring data and multiple standard clauses, avoiding excessive results increasing context pressure or too few results missing critical information |
similarity_threshold | 0.75–0.85 | Adapts to semantic matching accuracy for fields such as pollutant concentration and emission standards; too low a value will introduce irrelevant data, while too high a value will miss compliance-related content |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Covers parsing time for large emission inventory tables and engineering feasibility study reports, avoiding timeout interrupts during complex document parsing |
incremental_update_enabled | Enabled | Adapts to the high-frequency update needs of real-time monitoring data, reducing resource consumption from full uploads |
embedding_batch_size | 32–64 | Improves processing efficiency for batch uploads of structured monitoring data entries, avoiding delays from single-item processing |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Phenomenon: When specifying a query for an uploaded document in the workspace, the system prompts no matching results. Cause: Field-level precise recall configuration is not enabled, or the similarity threshold is set too high, causing semantic segments of the document to not be matched.
- Phenomenon: After uploading a large air emission inventory table, a parsing task timeout error is triggered. Cause: The value set for
PARSE_FILE_TIMEOUT_SECONDSis less than the actual parsing time, and the timeout parameter was not adjusted for structured table documents. - Phenomenon: The knowledge base cannot upload new air pollution control-related documents, and the system prompts insufficient storage capacity. Cause: The knowledge base upper limit configuration was not adjusted based on the vector database type in use; the pgvector default upper limit restricts the number of files, and no expansion or storage solution switch was performed.
How to Verify Proper Configuration
- Upload a single typical air monitoring data document, run a retrieval test, and verify that the recalled results include the target fields and content.
- Upload a large structured emission inventory, check the parsing task status to confirm no timeout errors occur.
- Set multi-condition filter retrieval, and verify that the recalled results only include content related to the specified monitoring locations or emission standards.
- View the knowledge base update logs to confirm that incremental update tasks trigger at the preset frequency and real-time data is synchronized promptly.
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.