What This Category's Data Looks Like
Data related to environmental monitoring financial reports comes from three main sources: annual environmental monitoring bulletins publicly released by ecological environment authorities, annual environmental reports disclosed by pollutant discharging enterprises, and compliance testing documents issued by third-party testing institutions. Core data update cycles follow an annual schedule. Real-time monitoring data for some key pollutant discharging locations is synchronized daily. Most documents include standard fields: monitoring location code, pollutant type, measured concentration, testing time, and compliance judgment result. Common units follow metering standards such as mg/m³, mg/L, and tons/year. Some documents also include monitoring method descriptions and compliance threshold comparison tables.
What Constraints These Characteristics Impose on Knowledge Base Retrieval and Recall
The multi-source, scattered nature of environmental monitoring financial report data requires the retrieval link to build unified indexes across data sources. This avoids missing compliant data from different channels. Data with different update cycles has distinct timeliness requirements for recall. Real-time monitoring data must prioritize short-term financial report analysis needs. Annual summary data supports long-term compliance assessment. Documents with rich fields and standardized units require retrieval to support field-level precise matching and unit normalization. This prevents invalid recall results caused by mismatched units. Threshold comparison content in some documents must be included as a recall association dimension. This assists compliance judgment during financial report analysis.
How to Configure
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall_top_k | 8-12 results | Environmental monitoring financial report data has many fields and moderate information per document. Too many recall results will exceed the large model's context window, while too few will fail to cover all analysis dimensions |
similarity_threshold | 0.75-0.85 | Environmental monitoring data has many professional terms. A matching threshold must be maintained to ensure semantic similarity between recall results and queries, and avoid introducing irrelevant general environmental protection data |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | A single annual environmental monitoring report may include multiple pages of monitoring tables and compliance descriptions. Parsing takes a long time, so sufficient time must be reserved for text splitting and index construction |
chunk_size | 800-1200 characters | Professional descriptions in environmental monitoring data are lengthy. Too short chunks will break the logical connection between monitoring indicators and compliance judgments, while too long chunks will reduce retrieval accuracy |
UPLOAD_FILE_MAX_SIZE | 200 MB | Annual environmental financial reports may include raw data files for batch monitoring locations, so support for large-sized document uploads is required |
rerank_top_k | 4-6 results | Perform secondary reranking on initial recall results to filter out entries with weak semantic relevance, to meet the precision requirements of financial report analysis |
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. Each scenario requires separate analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Symptom: The knowledge base backend can retrieve matching environmental monitoring data, but calling the large model produces no valid output, or the output does not include the retrieved monitoring indicators. Cause: The recall count is set too high, or the
maxContextparameter value is too small, resulting in a large amount of recalled data that cannot be fully included in the large model's generation context. - Symptom: For a single document containing only dozens of monitoring data entries, knowledge base retrieval takes more than 15 seconds. Cause: The
chunk_sizeparameter value is too small, splitting the document into too many chunks. Retrieval requires traversing a large number of chunk nodes, which increases retrieval latency. - Symptom: After offline deployment upgrade, the knowledge base retrieval results include a large number of irrelevant general environmental protection content, failing to match the precision requirements of financial report analysis. Cause: The dedicated knowledge base vector index for environmental monitoring was not rebuilt after the upgrade, and recall matching still uses the pre-upgrade general dataset.
How to Confirm Proper Configuration
- Upload a single annual environmental monitoring report, check the parsing task status log, confirm that there are no timeout errors during parsing, and that the configuration item settings are matched.
- Enter a query containing a specific pollutant name and unit, verify the field matching degree of the retrieval results, and confirm that the recall results include the target monitoring indicators and compliance judgment content.
- Test data sources with different update cycles, confirm that the priority of retrieval results meets analysis needs, and adjust the recall count and similarity threshold to calibrate matching precision.
- View the knowledge base index construction records, confirm that all uploaded environmental monitoring documents have completed vector indexing, and there are no parsing failed entries.
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.