What the data for this category looks like
Environmental monitoring intelligent due diligence report data mainly comes from four sources: fixed-point sensors, mobile inspection equipment, satellite remote sensing images, and public reports from third-party environmental monitoring institutions. Data update frequencies cover three levels: real-time second-level, hourly, and daily. Some abnormal monitoring data will trigger immediate reporting. The document structure of a single due diligence report includes fields such as monitoring point number, monitoring time, pollutant concentration, equipment operating status, and on-site inspection remarks. Units include various types such as μg/m³, mg/L, ℃, and %. Some reports also link aerial images of corresponding points and historical monitoring comparison data.
What constraints do these characteristics impose on the vector models and indexing link
The high-frequency, multi-rhythm update rhythm requires the index to support a hybrid mode of incremental updates and scheduled full updates, to avoid excessive cluster resource consumption from full reconstruction. Structured data with multiple fields and units requires the vector model to support semantic vectorization of both text and numerical fields, or requires converting structured data to a standardized text format before vectorization. Document structures that link images and historical data require the index to support hybrid retrieval of text vectors and image vectors, and to enable fast filtering by fields such as monitoring time and point ID. In addition, retrieval requirements for due diligence reports usually cover specific time ranges and regions. Relying solely on semantic similarity cannot meet precise retrieval needs, so the index must support precise filtering based on structured fields.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
embedding_model | Zhipu Embedding-3, Qwen Embedding | Supports semantic understanding of long text and structured data, with stable vectorization effects for numerical fields |
chunk_size | 800–1200 characters | Single structured environmental monitoring record is relatively short. This segment length avoids mixing unrelated monitoring items while ensuring semantic integrity |
index_type | HNSW | Adapts to high-frequency updated monitoring data, providing low-latency approximate nearest neighbor retrieval performance |
recall_top_k | Top 10–15 entries | Due diligence reports need to cover recent monitoring data from multiple points. This number of recalled entries balances retrieval efficiency and result comprehensiveness |
filter_field | Monitoring time, point ID | Enables fast filtering of recalled results based on time range and region parameters in retrieval requests |
cache_ttl | 3600 seconds | Monitoring data in the same region changes little over a short period. Caching reduces repeated query pressure on the vector database |
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 specific analysis, and it is recommended to test on independent samples before finalizing the configuration.
Three common mistakes
- Phenomenon: System response latency is high when querying due diligence reports for the same monitoring area repeatedly. Cause: No vector database caching mechanism is configured, and each query triggers a full vector retrieval.
- Phenomenon: After calling the "collection add data" function to upload due diligence report text, the vector index is not updated in a timely manner. Cause: Index construction is not triggered by creating a training order. Simply adding data will not automatically generate a vector index.
- Phenomenon: After uploading a due diligence report containing aerial images, the associated images are not returned in the search results. Cause: Image vectorization configuration is not enabled, and vectors are only generated for text content.
How to confirm the configuration is correct
- Perform a batch import of historical environmental monitoring data, check the index construction logs in the system backend, and confirm there are no errors and the progress meets expectations.
- Initiate two queries for the same monitoring region and time range, compare the response times of the two interfaces, and confirm that the caching mechanism is working.
- Add structured field filtering rules to the retrieval configuration, initiate a query with a specified region and time, and confirm that only qualifying data is recalled.
- Upload a test due diligence report containing aerial images, and confirm that the search results return both text-related information and corresponding images.
- Enter the model configuration page, check if the target embedding model has been added and can be selected normally, and confirm that the model access configuration is correct.
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.