What the Data for This Category Looks Like
Internal office data for the fertilizer and pesticide industry mainly comes from formula documents released by agricultural research academies, agricultural materials sales ledgers, field trial records, pesticide registration certificate documents, fertilizer nutrient test reports, and similar sources. Data update rhythms fall into three categories: newly registered pesticides and new formulas are updated monthly, annual field trial data is updated quarterly, and daily sales and usage ledgers are updated daily. Document structures include long-text trial reports (up to tens of thousands of characters per document), structured ledgers with fields such as product name, active ingredient, content, applicable crops, application dosage, and more. Units involve professional measurement standards such as g/L, %, g/mu, mL/hectare.
Constraints Imposed by These Characteristics on Deployment and Upgrade
Long-text field trial reports and pesticide registration documents require adjustments to chunk processing length to avoid semantic fragmentation. Professional fields in structured ledgers need pre-configured mapping rules to ensure correct association of technical terms during vector retrieval. High-frequency updated daily data requires scheduled incremental sync tasks to prevent excessive resource usage from full synchronization. Large-volume test report files raise storage configuration upper limits. During upgrades, compatibility logic for vector indexes of existing data must be retained to avoid invalidation of historical professional data.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 800–1200 connections | Fertilizer and pesticide data includes long-text trial reports and structured ledgers, requiring adaptation to high-concurrency data processing requests |
MONGODB_URI | mongodb://[username]:[password]@[host address]:27017/fastgpt_crop?authSource=admin | Stores metadata of professional data and vector index association data, requires a dedicated business database |
REDIS_URL | redis://:[password]@[host address]:6379/1 | Caches technical term vocabularies and user session contexts, requires stable low-latency connections |
STORAGE_S3_ENDPOINT | http://[storage server address]:9000 | Stores large-volume pesticide registration documents and field trial reports, requires support for large file uploads |
ROOT_KEY | Generate a random 32-bit string based on the deployment environment | Ensures access permission security of background management interfaces and prevents unauthorized operations |
FILE_TOKEN_KEY | Custom random secret key | Encrypts access tokens for file uploads and prevents unauthorized access to professional documents |
Three Common Configuration Mistakes
- Phenomenon: After local deployment, upload fertilizer or pesticide related documents, and the data processing steps show as empty. Cause: The
DB_MAX_LINKconfiguration is not adjusted for long-text and structured data, resulting in database connections being released early during large file processing. - Phenomenon: After packaging the locally deployed service into a Docker image, all vector retrieval scores are identical. Cause: The database index of
REDIS_URLis not correctly configured, resulting in duplicate overwrites of cached embedding vectors. - Phenomenon: After calling a local large model, the first response is normal, but subsequent requests with multi-turn contexts return errors. Cause: A valid
FILE_TOKEN_KEYis not set, resulting in failed verification of file references in session contexts.
How to Verify Proper Configuration
- Run the built-in database connection test tool and check whether the active connection count of the
DB_MAX_LINKconfiguration reaches the lower limit of the preset range. - Upload a field trial report longer than 10,000 characters and check whether the data processing steps generate valid chunks and vector indexes.
- After starting the Docker container, access the frontend address configured in
FE_DOMAINand verify whether the login permission of the background management interface takes effect normally. - Trigger an incremental sync task and check whether structured ledger data is correctly synced to the target knowledge base.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.