What data for this category includes
Water treatment marketing content data is sourced primarily from equipment technical documents, project delivery case studies, industry water quality standard documents, and common end-user consultation records. Data update frequencies are divided into two groups: product parameter data is updated quarterly with equipment iterations, industry standard data is updated annually, and user consultation data is updated monthly. Document structures mostly include structured tables (such as equipment model, processing capacity, energy consumption), long-form technical descriptions, and question-answer pairs with embedded parameters. Fields include equipment model, rated processing capacity, influent water quality indicators, maintenance cycle, and others, each with dedicated units. Some documents also include parameter curve charts.
Constraints imposed on deployment and upgrade by these characteristics
The multiple update frequencies, high proportion of structured content, and dedicated unit fields of water treatment marketing content impose clear constraints on the deployment and upgrade process. Multi-frequency layered updates require configuring automated synchronization tasks with matching cycles, to accommodate quarterly equipment parameter updates, annual industry standard updates, and monthly user consultation data updates respectively. The high proportion of structured parameter tables requires enabling FastGPT’s structured parsing mode, to prevent loss of core parameters such as equipment model and processing capacity caused by generic text splitting. Dedicated unit fields require configuring unit verification rules to avoid unit mismatch issues during import. The typical length of long-form technical descriptions also requires adjusting the context window to fit the required parameters.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_STRUCTURED_TABLE | Enabled | Water treatment marketing content contains a large number of equipment parameter tables. Enabling this setting preserves the correspondence between fields and units |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Water treatment technical documents often include multi-page equipment manuals and case collections, requiring support for large file uploads |
SYNC_DATA_INTERVAL | Split by data type: Set to 86400 seconds for equipment parameter data, 2592000 seconds for industry standard data, and 259200 seconds for user consultation data | Matches the update rhythms of different data types, avoiding excessive synchronization that occupies system resources |
maxContext | 8000–12000 characters | Water treatment technical descriptions are typically lengthy, requiring retention of complete parameter descriptions to avoid truncation of critical information |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Water treatment documents contain multi-page content and chart parsing, requiring sufficient parsing time to be reserved |
MODEL_CHANNEL | Custom channel | Adapts to docking requirements for locally deployed models or third-party proxy services, aligning with the configuration logic of version 4.9.1 |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, 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 mistakes
- Issue: In a locally deployed FastGPT workflow, a custom JavaScript code execution node returns a
500 Internal Server Error, and logs show that dependent modules are not found. Cause: The deployment environment does not have the third-party libraries required by the code execution node installed, or the docker mount path does not correctly map the dependency package directory. - Issue: After configuring
MODEL_CHANNELas the official channel, a model call returns a403 Forbiddenerror. Cause: The aiproxy key and interface address are not correctly configured, preventing normal authentication for the official model channel. - Issue: Water treatment documents imported via file upload cannot automatically synchronize updated changes to the original files afterward. Cause: The corresponding mount path is not configured in docker-compose.yml, or the automatic update switch for the knowledge base is not enabled. Only URL imports support automatic synchronous updates.
How to confirm the configuration is complete
- Upload a water treatment equipment manual that includes structured tables, and check if the parsed knowledge base document retains complete field and unit information.
- Manually trigger a configured synchronization task, and check if updated content from the corresponding data source is successfully synchronized to the knowledge base.
- Run a workflow node containing JavaScript code, and verify that the system logs have no dependency missing errors and that the returned results match the preset logic.
- After configuring a custom model channel, initiate a model call request, and check that the interface returns normally with no authentication exceptions.
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.