What the data for this category looks like
Aquaculture data for financial investment research scenarios comes from multiple sources: public monitoring data released by the Ministry of Agriculture and Rural Affairs Fishery Administration, on-site records from local aquatic technology promotion stations, transaction quotes from aquatic product circulation associations, internal production ledgers from aquaculture enterprises, and breeding technical specifications from research institutes.
Update rhythms vary significantly by data type:
- Water quality monitoring data updates hourly
- Transaction quotes update per trading day
- Industry statistical reports are released monthly
- Technical guides have irregular revision cycles
Documents include structured breeding parameter tables, semi-structured monitoring logs, and unstructured research papers and policy documents. Core fields and their corresponding units are:
- Breeding area ID: no unit
- Monitoring time: yyyy-MM-dd HH:mm:ss
- Dissolved oxygen concentration: mg/L
- Water temperature: ℃
- Feed feeding amount: kg/mu·day
- Seedling stocking amount: individuals/mu
What constraints these characteristics impose on deployment and upgrade
The multi-source, heterogeneous nature of aquaculture investment research data requires pre-deployment parsing plugins adapted to different data sources. During upgrades, compatibility with newly added aquaculture environment monitoring device protocols must be maintained to avoid interruptions to the data access link.
A high proportion of structured table documents requires preset dedicated field mapping rules during deployment. During upgrades, the built-in table parsing logic must not be modified arbitrarily. Doing so will cause exceptions in historical data parsing.
The update frequencies of different data sources vary significantly. During deployment, independent synchronization trigger cycles must be configured for each data source. During upgrades, the original synchronization configuration migration logic must be retained to avoid data synchronization coverage errors caused by batch adjustments.
Additionally, field unit unification requirements for aquaculture investment research data are strict. Unit conversion mappings must be pre-configured during deployment, and relevant parsing rules must be updated synchronously during upgrades.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Aquaculture monitoring logs and structured reports typically contain multiple columns and long text content, requiring longer parsing time to complete format conversion and field extraction |
UPLOAD_FILE_MAX_SIZE | 1500–2000 MB | Monthly batch-uploaded aquaculture environment monitoring CSV files and transaction quote reports have large file sizes, requiring adaptation to large-file upload requirements |
Chunk Length | 800–1200 characters | The paragraph lengths of aquaculture technical documents and research reports vary widely. This range balances vector recall accuracy and context completeness |
Recall Count | Top 6–8 results | Investment research scenarios require a balance between information comprehensiveness and result readability. This quantity can cover core breeding parameters and market data |
Similarity Threshold | 0.72–0.85 | Low-correlation aquaculture environment data and unrelated industry reports need to be filtered. This range matches the information matching accuracy required for aquaculture investment research |
PLUGIN_LOAD_TIMEOUT | 120 seconds | Loading third-party investment research data synchronization plugins during private deployment requires sufficient time to avoid deployment failures caused by plugin initialization timeouts |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, 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 errors
- Symptom: Docker image pull failure, with logs returning
network timeoutormanifest unknown. Cause: Domestic image acceleration sources are not configured, or the used FastGPT 4.9.0 image has been removed by the official team, and the historical version image address was not obtained in advance. - Symptom: Importing plugins during private deployment of V4.14.1 version prompts
internal server error. Cause: The plugin configuration file contains unescaped special characters, or server memory is insufficient, causing the plugin loading process to be terminated by the system. - Symptom: After batch uploading aquaculture monitoring log files, some fields in the parsing results are empty. Cause: Dedicated field mapping rules for aquaculture data are not pre-configured, and the default parsing logic cannot recognize exclusive fields such as breeding area ID and dissolved oxygen concentration.
How to confirm the configuration is complete
- Perform a local file upload test: Upload a standard aquaculture monitoring CSV file, and check whether the parsed fields match the preset mapping rules.
- Check the server's Docker container logs to confirm that the
PARSE_FILE_TIMEOUT_SECONDSconfiguration does not trigger timeout errors, and there are no abnormal exit records for the plugin loading process. - Initiate an investment research query, and check that the number of returned recall results matches the value range configured for
Recall Count, with no abnormally low similarity results included. - Test the version upgrade process: Upgrade the current deployment version to the specified version, confirm that the original synchronization tasks and field mapping rules have not changed, and the data synchronization link is operating normally.
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.