What the data for this category looks like
Water treatment research data mainly comes from real-time water quality data from environmental monitoring stations, equipment operation logs from water utility operators, national and industry water quality standard documents, and laboratory reports from third-party testing institutions. Real-time monitoring data updates every minute. Equipment operation logs are archived daily. Standard documents are released irregularly alongside policy updates. Single documents usually include structured fields such as monitoring point codes, collection timestamps, core water quality indicators (COD, ammonia nitrogen, pH, etc., with units mg/L, dimensionless), treatment process parameters, and equipment runtime. Some associated documents also include sampling site coordinates and upstream and downstream hydrological data.
What constraints these characteristics impose on database and operations
Real-time minute-level data creates high-frequency write pressure. Create a joint index for monitoring point ID and timestamp fields to avoid write blocking. Coexisting multiple update rhythms requires layered data storage. Separate real-time stream data and archived log data to reduce cold data operation costs. Structured fields include water quality indicators with multiple units. Add unit verification rules before data import to avoid data confusion. Mixed document types include structured monitoring data and non-standard process descriptions. Configure two storage adaptation rules to write to relational databases and object storage respectively. Data scale grows linearly with monitoring point expansion. Reserve horizontal expansion space for database cluster configurations to adapt to subsequent business growth.
How to set the configurations
| Configuration Item | Recommended Value | Basis for This Setting |
|---|---|---|
MONGODB_URI | mongodb://[username]:[password]@[host_address]:[port]/fastgpt?authSource=admin | Adapts to FastGPT's official MongoDB connection format, supports local or remote deployment |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Single water quality test report or process document usually does not exceed 1.8GB, with reasonable buffer reserved |
recall_count | Top 10 entries | Single research document contains multi-dimensional water quality indicators. More related fragments need to be covered to ensure comprehensive retrieval |
similarity_threshold | 0.75–0.85 | Balances the accuracy of water quality indicator matching and recall coverage, avoids missing key process parameters |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Large operation logs or standard documents take longer to parse, avoids task interruption due to timeout mid-process |
database_sharding_threshold | 500 GB | Water treatment data grows quickly with the increase of monitoring points, automatically triggers sharding expansion when the threshold is reached |
The parameter values provided on this page are all conventional recommendations used to determine the 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 errors
- Phenomenon:
Authentication failederror appears when connecting to MongoDB, or connection timeout prevents session establishment. Cause: Incorrect authentication source parameter inMONGODB_URI, or inbound firewall rule for MongoDB default port 27017 not opened for local Windows deployment. - Phenomenon: After upgrading from v4.6.7 to v4.8.10, some historical monitoring data has missing fields or incorrect unit parsing. Cause: Database table structure definitions were not exported in advance, and the database directory was migrated directly. New and old versions have different field mapping rules, resulting in data that cannot be parsed normally.
- Phenomenon: When initiating a research query, no knowledge base retrieval is triggered, and preset answers are returned directly. Cause: Retrieval trigger conditions are incorrectly configured, or the similarity threshold is set too high, resulting in documents that meet matching conditions not being recalled.
How to confirm configuration is valid
Run the database connection test script to check if the MONGODB_URI configuration can establish a normal connection, and verify if the returned connection status code meets expectations. Upload a typical water quality test report to confirm that parsed fields and units conform to preset rules, and check if the parsing task status is "Completed". Initiate a research query targeting a specific water quality indicator, verify the matching logic between the number of recalled documents and the similarity threshold, and confirm that retrieval results include target data. Check the database table structure definition tool to confirm that the type and unit verification rules of each business field have taken effect as configured, with no missing fields.
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.