What the data for this category looks like
Aquaculture marketing content data primarily comes from pond daily monitoring records, feed feeding ledgers, water quality test reports, seed breeding archives, industry trend documents, and breeding case materials. Data update frequencies cover real-time (monitoring items such as pond dissolved oxygen, water temperature), daily (feeding, disease records), weekly (industry trend weekly reports), and batch-level (harvest sales data). Document structures include structured tables (such as daily monitoring sheets per pond), case documents with charts, and industry standard texts. Field units are mostly professional measurement standards such as mg/L, ℃, kg/mu, tail/㎡.
What constraints do these characteristics impose on deployment and upgrade
The above data characteristics create multiple constraints for the deployment and upgrade link: First, documents with high-frequency updates and long-cycle logs require adjusting file parsing timeout configurations during deployment to avoid interruptions when parsing large-volume documents. Second, marketing materials with multi-format charts need structured parsing and multimodal support enabled. During upgrades, configuration migration for legacy parsing plugins must be supported. Third, professional fields with mixed units require unified field mapping rules to prevent unit confusion during retrieval. Fourth, synchronization requirements for real-time trend data require reserving external interface call resources during deployment. During upgrades, configuration entries for scheduled synchronization must be retained.
How to configure the settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Aquaculture documents include long-cycle water quality logs and cases with multiple charts. Single-file parsing takes a long time, and the default timeout duration cannot cover the full parsing process |
UPLOAD_FILE_MAX_SIZE | 1000 MB | A single breeding case document may integrate multiple monitoring sheets and high-definition on-site images. Single-file size exceeds the default limit |
maxContext | 800–1200 characters | Marketing content needs to balance professional breeding details and user comprehensibility. This segment length adapts to retrieval recall accuracy and contextual coherence |
similarity threshold | 0.75–0.85 | There are many professional terms in aquaculture. A higher matching degree avoids irrelevant content being recalled, adapting to the retrieval needs of segmented scenarios |
WHISPER_MODEL_PATH | Path of the locally deployed medium-sized model | Marketing content may include audio breeding training materials. Local deployment avoids risks related to external network dependencies |
SYNC_CRON_EXPR | 0 0 2 * * * | Breeding industry trend data is updated daily. Synchronizing every early morning ensures the timeliness of marketing content |
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 individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common errors
- Phenomenon: The page is blank after embedding an external access address via iframe. Direct access to the external address works normally. Cause: The
CORS_ALLOW_ORIGINSparameter was not configured during deployment, and the domain name whitelist for the embedded page was not added. - Phenomenon: Calling the
POST /v1/audio/transcriptionsinterface returns an error after upgrading to version 4.8.17. Cause: The loading path configuration for the local Whisper model was changed after the upgrade, and theWHISPER_MODEL_PATHparameter was not updated synchronously. - Phenomenon: Fields are missing or formatted chaotically after parsing batch-uploaded breeding monitoring sheets. Cause: The
PARSE_TABLE_STRUCTUREparameter was not enabled, and the table structured parsing function was not activated, resulting in failure to correctly extract table content.
How to confirm the configuration is correct
- Upload a single breeding document that meets the configured size limit, check if the parsing task completes within the set timeout period, with no parsing failure prompts.
- Initiate a knowledge base retrieval, verify that the matching degree of recall results meets business expectations. Adjust the similarity threshold to correct matching precision as needed.
- Load the FastGPT application via an embedded test page, confirm that the page displays normally with no cross-domain related errors.
- After upgrading, check the local model's running logs, confirm that the
WHISPER_MODEL_PATHparameter takes effect, with no model loading failure prompts.
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.