What the data for this category looks like
Data for solid waste treatment intelligent due diligence reports comes primarily from environmental impact assessment approval documents, hazardous waste disposal ledgers, solid waste haulage delivery receipts, ecological environment department supervision public notices, and third-party test reports. Update rhythms vary greatly: haulage records are updated daily, qualification documents are updated annually, and supervision public notices are synchronized in real time. Most individual documents mix structured and semi-structured content, including fields such as project subject, disposal qualification number, monthly disposal total, pollutant type, compliant test value, and disposal process. Units mostly use industry standard measurement methods such as tons, mg/m³, mg/L, and similar standards.
What constraints these characteristics impose on the knowledge base retrieval and recall link
Solid waste treatment due diligence data has a high proportion of structured content, large differences in update rhythms, and dense industry-specific terminology. These factors impose multiple constraints on the retrieval and recall link. Structured fields require support for exact matching and numerical range queries to meet precise retrieval needs for fields such as qualification numbers and disposal volumes. Data sources with multiple update cycles need batch incremental synchronization tasks configured to avoid excessive resource occupation from full synchronization. Industry-specific terms such as HW series hazardous waste categories and incineration/landfill disposal processes require dedicated vocabularies to optimize semantic recall and reduce incorrect term matching. Individual documents have large volume, so segmentation rules must be adjusted to adapt to long text splitting and avoid context overflow.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall count | top 10-15 | Solid waste due diligence data has many fields, so enough associated documents must be covered to avoid missing key information such as qualifications and disposal volumes |
similarity threshold | 0.72-0.85 | There are many industry-specific terms in the solid waste sector. A threshold that is too low will introduce irrelevant documents, while a threshold that is too high may miss valid professional matching results |
rerank return count | top 5-8 | Due diligence reports need to focus on core compliance information, so recall results should be simplified to adapt to large model context windows |
segment length | 800-1200 characters | Most solid waste documents contain long sections of test data and process descriptions. This length retains complete semantic units while avoiding overly long single segments |
incremental sync cycle | configured per data source type | Haulage records are updated daily, while qualification documents are updated annually. Differentiated cycles adapt to the update rhythms of different data sources |
field retrieval switch | enabled | Solid waste data includes structured fields such as qualification numbers and disposal volumes. Enabling this allows precise field matching |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Errors
- Symptom: After the rerank model is configured, the
is_re_rankfield in retrieval results returnsfalse. Cause: The application's global rerank switch is not enabled, or the rerank model's call permissions are not bound to the current workflow. - Symptom: The variable reference dropdown menu of the knowledge base search node is blank, and dynamic knowledge base variables cannot be selected. Cause: A global variable of type "knowledge base" has not been created in application variables, or the variable has not been set to a scope that can be referenced by nodes.
- Symptom: The number of documents returned by retrieval is much lower than the configured recall count. Cause: The similarity threshold is set too high, or field retrieval is not enabled, resulting in ineffective matching of structured fields.
How to Confirm Proper Configuration
- Enter the knowledge base management page and check synchronization logs for each data source to confirm incremental synchronization tasks execute according to the configured cycle.
- Enter a query containing qualification numbers and disposal volumes on the application test page, and check if retrieval results include documents matching the corresponding fields.
- After enabling the rerank function, check the rerank score field of retrieval results to confirm scores are sorted as expected.
- After configuring the dynamic knowledge base variable, call the knowledge base search node in the workflow to verify created knowledge base variables appear in the variable dropdown menu.
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.