What the data for this category looks like
Aquaculture investment research data originates from multiple dimensional sources. Real-time data from pond environment monitoring devices. Daily feeding and seedling management logs. Disease detection and diagnosis reports. Industry market and feed cost data.
Update frequencies vary significantly. Environment monitoring data updates hourly. Aquaculture logs are archived daily. Disease reports are uploaded immediately. Industry market data updates daily.
Most documents use structured tables, with fields including pond ID, monitoring time, water temperature, dissolved oxygen concentration, and additional fields. Units include ℃, mg/L, kg/mu, tail/cubic meter, and others. Semi-structured diagnosis records and unstructured technical documents are also present.
What constraints these characteristics impose on model integration and configuration
The multi-dimensional and high real-time characteristics of aquaculture data create clear constraints for model integration and configuration.
Structured data accounts for a large share of total data. Models must support vector storage and associated recall for structured fields. Index fields centered on pond ID must be configured.
Large differences in data update frequencies require support for configuring sync refresh cycles per data source. This prevents delays for real-time data or expiration of historical data.
Unstructured diagnosis records and technical documents require models to support specific entity extraction. Examples include disease names and affected ponds. The model’s recall and parsing rules must be adjusted.
Cross-data source association analysis requires configuring recall logic for cross-table associations. This ensures complete data per pond dimension.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the average length of aquaculture structured logs and unstructured diagnosis records, avoiding truncation of key fields such as pond ID and dissolved oxygen values |
recallTopK | Top 8–12 entries | A single pond has a large volume of associated data. Sufficient historical feeding records, environment monitoring data, and disease cases must be recalled |
similarityThreshold | 0.72–0.85 | Aquaculture data fields have strong correlation. A threshold that is too low will introduce records from unrelated ponds. A threshold that is too high will miss similar disease diagnosis cases |
refreshInterval | 300–3600 seconds (adjust per data source) | Set environment monitoring data to 300 seconds, industry market data to 86400 seconds, matching the update rhythms of different data types |
vectorStoreIndexField | pond_id | Aquaculture data uses ponds as the core association dimension. Indexing by pond ID enables fast recall of all business data for a single pond |
INITIAL_ROOT_PASSWORD | Custom strong password, length ≥12 characters | Used to manage channel API access permissions. Must meet the authentication complexity requirements of the vendor. Corresponds to the environment variable configuration in docker-compose.yml |
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. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: The model displayed in the chat interface does not match the model configured in the backend. Cause: The
modelMappingparameter was not configured correctly, or the model alias set for the channel does not match the actual deployed model name. - Phenomenon: The API call returns a 401 Unauthorized error. Cause: The
INITIAL_ROOT_PASSWORDenvironment variable was not correctly written to docker-compose.yml, or the entered password does not match the authentication password required by the channel. - Phenomenon: Locally deployed Ollama models cannot be called by the platform. Cause: The Ollama 11434 port was not opened, or the correct service address
http://localhost:11434was not filled in the configuration.
How to Confirm the Configuration is Complete
- Log in to the platform’s model channel configuration page. Confirm that
modelTypematches the actual deployed model name, and that themodelAliasmapping configuration is correct. - Initiate a test call. Enter an aquaculture-related query statement. Check that the model identifier in the returned results matches the configured model. Also review logs for successful authentication prompts.
- Upload a structured pond feeding log. Verify that the vector recall results include associated pond data, and that the number of recalled entries matches the configured
recallTopKvalue. - View the platform container’s running logs. Confirm that there are no error messages related to authentication failures, connection timeouts, or parameter parsing errors.
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.