Deployment and Upgrade for Environmental Monitoring Research

What Data for This Category Looks Like

Environmental monitoring research report data comes from three main sources: real-time data from national and provincial controlled monitoring stations publicly released by ecological environment authorities, special analysis reports from third-party environmental monitoring institutions, and regional environmental quality bulletins published by industry associations. Data update frequencies fall into three categories: real-time (station monitoring data), quarterly (special reports), and annual (quality bulletins). Document structures include fields such as monitoring point number, pollutant name, concentration value, monitoring time, regional scope, and compliance judgment standards. Concentration values use units of micrograms per cubic meter (μg/m³) or milligrams per cubic meter (mg/m³). Some special reports include latitude and longitude coordinates for monitoring points.

Constraints Imposed by These Characteristics on Deployment and Upgrade

These data characteristics create clear constraints for deployment and upgrade workflows. High-frequency real-time station data requires vector databases to support streaming writing, which avoids delays caused by batch synchronization. Long-format special reports have significant length, so parsing workflows need to adapt to longer timeout thresholds and appropriate chunking strategies. Fields include standardized units and latitude/longitude coordinates. Deployments must configure metadata filtering rules to ensure retrieval only returns results matching required units and regions. Upgrade workflows must maintain compatibility with old and new metadata formats, which prevents field mismatches between existing datasets and newly imported data.

Configuration Recommendations

Configuration Item	Recommended Value	Rationale
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Environmental monitoring special reports have significant length. Parsing takes longer than default thresholds. Extending the timeout prevents parsing failures
`maxChunkSize`	`800–1200 characters`	Environmental monitoring research reports include many tables and numerical paragraphs. This chunk length preserves contextual connections and avoids breaking data logic
`RECALL_TOP_N`	`Top 8–12 results`	Environmental monitoring data covers multiple points and pollutants. Retrieving sufficient results covers user needs for regional and indicator queries
`SIMILARITY_THRESHOLD`	`0.75–0.85`	Filters irrelevant monitoring data while retaining valid results for the same region and pollutant
`EMBEDDING_MODEL`	`Alibaba-emb3`	Environmental monitoring research reports include many structured numerical values and regional information. This model is optimized for professional domain text, which improves semantic matching accuracy
`UPLOAD_FILE_MAX_SIZE`	`1000 MB`	Large annual environmental monitoring reports have significant file size. Relaxing upload limits supports complete document imports

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

Retrieval results have unexpected semantic matching. The cause is failure to set EMBEDDING_MODEL to Alibaba-emb3. Using the default model leads to matching deviations.
Containers return a 504 Gateway Timeout error after startup. The cause is failure to adjust the PARSE_FILE_TIMEOUT_SECONDS parameter. Parsing long environmental monitoring reports takes longer than the default threshold.
Containers exit due to insufficient disk space. The cause is failure to clean temporary parsing files in the /var/lib/docker/volumes/fastgpt directory and failure to configure automatic cleanup rules.

How to Confirm Proper Configuration

Upload a standard quarterly environmental monitoring report. Check if the number of parsed chunks matches expectations. Adjust maxChunkSize until the chunking logic matches the document structure.
Run a retrieval query for a specific monitoring point and pollutant. Check if returned results include matching monitoring point number and pollutant concentration unit fields. Adjust SIMILARITY_THRESHOLD to control result relevance.
View container runtime logs. Confirm the EMBEDDING_BATCH_SIZE setting does not trigger out-of-memory errors. Adjust the batch size to match current hardware resources.
Modify the frontend access address to use HTTPS format. Confirm the page loads normally with no mixed content warnings. Verify the SSL certificate and URL configuration take effect.

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.