What the data for this category looks like
Data sources for commodity trade ledger data include port warehouse systems, inventory ERP systems, settlement bank documents, and scanned trade contracts. Updates sync after each transaction settlement or at the end of each business day. Most documents are structured tables, with fields including goods category, batch number, gross weight, net weight, unit price, delivery date, counterparty, storage location, and more. Common units are tons, yuan per ton, and cubic meters. Some attachments are PDF or image format original vouchers, with large individual file sizes.
Constraints on Deployment and Upgrade
Multi-source heterogeneous data structures require adapting both relational databases and non-relational storage during deployment. High-frequency updated ledger data requires configuring appropriate caching strategies to avoid overloading the core database. Large-volume transaction voucher attachments require expanding storage node bandwidth and capacity to prevent upload timeouts. Complex field combinations require retaining existing database indexes during upgrades to avoid data migration failures. Cross-container deployment requires unifying access keys for storage and databases to prevent access exceptions caused by inconsistent permissions between nodes.
How to Set Configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://admin:secure_pass@db-host:27017/commodity_ledgers?authSource=admin | Commodity trade ledgers contain nested transaction details; MongoDB is suited for unstructured and semi-structured data storage |
PG_URL | postgresql://user:secure_pass@db-host:5432/ledger_metadata | Structured ledger fields such as goods category and delivery date are suitable for PostgreSQL storage, which supports complex queries |
REDIS_URL | redis://:cache_pass@redis-host:6379/0 | Cache frequently queried daily ledger aggregation data to reduce database access pressure |
STORAGE_S3_ENDPOINT | https://s3-cn-north-1.amazonaws.com | Adapt to large-volume transaction voucher attachment storage to meet the attachment transmission needs of commodity trading |
FILE_TOKEN_KEY | 32-bit randomly generated secure string | Generate authentication tokens for uploaded ledger attachments to prevent unauthorized access |
ROOT_KEY | Custom complex administrator key | Used for identity verification for background configuration and system-level operations |
Three Common Misconfigurations
- Symptom: Ledger attachments uploaded after image-based deployment cannot be identified, with no error messages. Cause:
STORAGE_S3_ENDPOINTor the corresponding storage access key is not configured correctly, preventing the container from establishing a connection with the attachment storage service. - Symptom: Code plugin runs return AxiosError 404. Cause:
FE_DOMAINor the configuration address of the internal interface is incorrect, causing the request path to not match the actual service address. - Symptom: AI chat nodes in advanced orchestration cannot obtain the initial ledger query question submitted by the user. Cause: The initial user input is not bound to the context variable of the orchestration node, causing data transfer interruption between nodes.
How to Verify Configurations
- Enter the FastGPT container, run the redis-cli ping command to verify that the cache service corresponding to
REDIS_URLis connected normally. - Upload a single 500 MB ledger scanned document to check if the system generates a valid accessible attachment link.
- Call the built-in database query interface to verify that both
PG_URLandMONGODB_URIcan normally read structured and unstructured ledger data. - Log in to the system background using
ROOT_KEYto confirm that all configuration items have been saved and there are no abnormal prompts.
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.