What the data for this category looks like
Data sources for water treatment marketing content include internal water quality test logs, water treatment equipment technical manuals, past project acceptance reports, marketing copy, and user inquiry records. Updates follow adjustments to industry compliance standards, and trigger on new equipment launches or project completions. No fixed update cycle exists. Three document structures are included: structured parameter tables with water quality indicators with units such as COD, pH, mixed text-image project case studies, and standardized customer service and marketing scripts. Fields include equipment model, installation location, test date, applicable scenario, and some have clear units such as mg/L, and dimensionless pH values.
Constraints on vector models and indexing from these characteristics
The mixed structure and field properties of water treatment marketing content create multiple constraints for the vector model and indexing workflow. Structured parameter tables must retain semantic relationships between fields, to avoid losing the binding between water quality indicators and equipment models after splitting. Irregular update cycles require support for incremental indexing, to avoid resource consumption from full reindexing. Coexistence of short scripts and long technical documents with varying content lengths requires flexible adjustment space for segmentation parameters. Numeric fields with units must be accurately mapped to semantic vectors, to avoid retrieval bias caused by separation of units and values. Timeliness requirements for compliance documents require tracking document updates and automatically triggering reindexing, to prevent expired content from being retrieved.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the mixed structure of water treatment marketing content, which includes both short scripts and long technical documents. Avoids semantic fragmentation from overly short chunks, and reduced vector accuracy from overly long chunks. |
chunk_overlap | 100–150 characters | Retains associated information between segments, adapts to content with cross-segment associations such as water quality indicators and equipment parameters, and avoids losing context after splitting. |
embedding_batch_size | 32–64 | Adapts to the scale of batch processing water treatment documents, balances indexing speed and memory usage, and avoids timeouts from large batch processing. |
similarity_threshold | 0.72–0.80 | Adapts to the semantic accuracy requirements of water treatment marketing content, filters low-relevance non-target content, while retaining the recall rate of compliance documents. |
recall_top_k | Top 6–8 results | Matches the length of user queries for water treatment project inquiries, avoids interference from excessive irrelevant content, and covers marketing needs for multiple scenarios. |
enable_incremental_index | Enabled | Adapts to the irregular update cycle of water treatment documents triggered by compliance updates and new equipment launches, avoiding resource consumption from full reindexing. |
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 case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Symptom: After batch uploading water treatment marketing documents, batch vector training cannot be triggered. Only individual document uploads complete the indexing process. Cause: The
enable_batch_embeddingconfiguration is not enabled, or the "batch vectorization" option is not selected during batch upload. - Symptom: After uploading a single water treatment document, it shows 8 segments, then changes to 13 segments after a period of time, with duplicate segmented content. Cause: The
chunk_overlapparameter is set beyond the effective length proportion of the document, and the document contains duplicate compliance standard text. The system captures the same content block repeatedly during segmentation. - Symptom: After upgrading from version 4.9.0 to 4.9.3, previously queryable water treatment marketing content can no longer be retrieved. Cause: The default vector model version is changed after the upgrade. Vector embeddings generated for old documents cannot match the semantic space of the new model. Vector embeddings for corresponding documents must be regenerated.
How to Confirm Configurations Are Properly Set
- Upload a single water treatment document containing structured parameters. Check whether background parsing logs correctly extract fields such as water quality indicators and equipment models, to confirm that structured data parsing configurations are effective.
- Initiate a batch indexing test, verify that all batch uploaded documents complete vector generation and indexing, with no failed entries.
- Enter a simulated user query, such as "COD removal rate of a certain model of water treatment equipment", check the number of segments and content matching degree of recall results, adjust relevant parameters to meet business requirement ranges.
- Edit and update an already indexed water treatment document, verify that the system automatically triggers incremental indexing, without requiring manual re-upload of the full document.
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.