What the data for this category looks like
The data for solid waste treatment intelligent due diligence reports primarily comes from project approval documents, daily operation and maintenance logs, environmental protection supervision announcements, and third-party test sampling reports. There are three update cycles: Project approval documents are one-time archived data. Operation and maintenance logs are updated daily or weekly. Supervision announcements are updated quarterly. A single document usually includes basic project information, treatment process parameters, feed and discharge records, compliance test results, operation and maintenance exception logs, and other content. Fields include project number, treatment scale (unit: tons/day), single feed amount (unit: kilograms), equipment operating duration (unit: hours), test indicator values (unit: mg/L), and other items. There is no fixed uniform range for document length.
What constraints do these characteristics impose on the vector models and indexing link
The daily incremental update feature of operation and maintenance logs requires the index to support incremental updates. Otherwise, it will consume a large amount of computing resources. Documents contain a mixed structure of structured numerical fields and unstructured operation and maintenance text. It is necessary to distinguish the encoding logic of the two types of data to avoid losing structured information with a single encoding method. Field units are unified but types are diverse. Vector indexing must avoid matching deviations caused by unit differences. The length of single documents varies significantly, ranging from a few pages of compliance reports to dozens of pages of operation and maintenance logs. It is necessary to dynamically adapt segment length to ensure information integrity.
How to set the configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Solid waste treatment due diligence reports include structured parameters and long-text operation and maintenance records. This range balances the integrity of single-segment information and recall accuracy |
chunk_overlap | 100–150 characters | Avoid cutting core structured fields during segmentation. The overlapping length adapts to the context connection requirements of long-text segmentation |
vector_store.index_type | HNSW | Adapts to high-dimensional vector retrieval for the mixed characteristics of solid waste data, balances retrieval speed and recall rate, and supports incremental updates |
similarity_threshold | 0.72–0.85 | Matching solid waste compliance test records requires high accuracy. This range balances the risks of false recalls and missed recalls |
retrieve_top_k | Top 6–8 results | Relevant information for due diligence reports is mostly distributed across multiple logs. An appropriate number of recalls can cover complete associated scenarios |
enable_incremental_index | Enabled | Adapts to the daily incremental update feature of operation and maintenance logs, avoiding resource consumption and service interruptions caused by full index reconstruction |
The parameter values provided on this page are all 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 configuration.
Three common mistakes
- Phenomenon: When deploying locally, the vector model has normal connectivity with oneAPI but reports an error during calling, returning
500 Internal Server Error. Cause: No dedicated vector model encoding parameters for solid waste data are configured. The default model does not adapt to the encoding logic of structured numerical fields. - Phenomenon: When importing due diligence reports, automatic segmentation cuts core compliance test fields, resulting in missing key parameters in recall results. Cause:
chunk_sizeis set too small, failing to adapt to the length requirements of structured fields. - Phenomenon: When incrementally updating operation and maintenance logs, full index reconstruction takes more than
3600 seconds, causing service interruptions. Cause: Theenable_incremental_indexconfiguration is not enabled, and the indexing strategy is not optimized for incremental data.
How to confirm the configuration is complete
- Upload a single operation and maintenance log document, check the segmentation results, and confirm that core structured fields are not cut.
- Initiate a similarity search, enter compliance test indicator values, and verify that the recall results include the corresponding project's operation and maintenance records.
- Submit an incremental update task, check the index update time, and confirm that full reconstruction is not triggered.
- Check the index type configuration of the vector database, and confirm that it matches the preset
HNSWtype.
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.