What the data for this category includes
Water treatment research and investment knowledge base data sources include real-time collected data from water monitoring stations, water treatment process standard manuals, water quality test reports, project operation and maintenance logs, and industry technical papers. Update frequency varies by type. Real-time monitoring data updates from once per second to once per hour. Standard documents are updated quarterly or annually. Project documents are updated as needed. Document structure covers three categories:
- Structured water quality indicator tables, with fields such as monitoring point ID, monitoring time, and pollutant concentration. Units include mg/L, pH value, and m³/h.
- Semi-structured process flow diagram descriptions and operation and maintenance records.
- Unstructured PDF standard documents and technical papers.
Constraints imposed on vector models and indexing
The multi-type and update characteristics of water treatment data impose multiple constraints on the vector models and indexing link. High-frequency real-time monitoring data requires indexes to support low-latency incremental updates, to avoid performance loss caused by full reconstruction. Structured data includes professional numerical fields with units. Vector models must be compatible with structured metadata encoding logic, to avoid semantic deviation caused by lost unit information. Document length ranges from tens of characters of monitoring alerts to tens of thousands of characters of process standards. An adaptive segmentation strategy must be configured, to avoid long text truncation or insufficient semantics of short texts. There are many industry-specific terms. Vector models must adapt to the encoding of professional vocabulary in the water treatment field, to improve recall matching accuracy.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Segmentation Length | 800–1200 characters | Covers the core semantics of water treatment process descriptions, adapts to the input length limits of most vector models, and avoids encoding deviation caused by overly long single segments |
Number of Recalls | Top 8–12 results | Balances recall coverage for professional literature and real-time monitoring data, controls context window overhead, and avoids redundant information interfering with research and investment analysis |
Similarity Threshold | 0.72–0.85 | Distinguishes semantic similarity of water treatment professional terms, and avoids confusing low-relevance water quality standards with operation and maintenance records |
ENABLE_INCREMENTAL_INDEX | Enabled | Supports incremental writing of real-time monitoring data, avoids time loss from full index reconstruction, and adapts to high-frequency update business scenarios |
Vector Model Type | Model supporting structured metadata encoding | Compatible with encoding of water quality indicator fields with units, and retains semantic integrity of professional data |
Number of Index Shards | Evenly distributed by number of deployment nodes | Balances index query load, and improves retrieval response speed for large-scale knowledge bases |
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 specific analysis, and testing should be conducted on local samples before finalizing settings.
Three common mistakes
- Phenomenon: A 401 unauthorized error is returned when calling the vector model. Curl testing works normally, but FastGPT calls fail. Cause: The vector model's API key or access whitelist is not configured correctly. FastGPT's request header does not carry valid authentication information.
- Phenomenon: Duplicate monitoring data entries remain after the knowledge base index is merged. Cause: Index deduplication configuration is not enabled, or the deduplication field does not use the combination of monitoring point ID and monitoring time. This results in duplicate data not being recognized.
- Phenomenon: FastGPT returns a call rejection when accessing a locally deployed vector model, while curl testing passes. Cause: The platform's proxy forwarding rules are not configured correctly. The requested port or protocol does not match the model deployment address.
How to verify correct configuration
- Upload a water treatment process standard document. Check whether the length of segmented text blocks matches the configured
Segmentation Lengthinterval. Segmentation results can be verified through platform logs. - Initiate a retrieval request related to water quality monitoring. Confirm whether the number of returned recall results matches the configured
Number of Recalls, to verify the index recall logic. - Submit new real-time monitoring data. Check whether the index completes incremental updates. Write status can be confirmed through index update logs.
- Call the vector model test interface. Verify that the returned vector data format matches the configured model dimensions, to confirm normal model access.
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.