What this category's data looks like
Basic chemicals ledger data mainly comes from production distributed control systems (DCS), raw material and finished product quality inspection reports, and material inventory and sales management systems. Data updates trigger per production shift or fixed daily time periods. Each single batch ledger includes structured fields and unstructured attachments. Structured fields include material batch number, feeding quantity (unit: ton or kilogram), reactor temperature (unit: ℃), pressure (unit: MPa), and qualified judgment result. Unstructured attachments are scanned paper quality inspection documents or original electronic test data for the corresponding batch. The length of a complete ledger document ranges from hundreds to thousands of characters.
What constraints these characteristics impose on the deployment and upgrade phase
Basic chemicals ledgers have many structured fields, high data update frequency, and include unstructured attachments. Deployment and upgrade processes must adapt to three types of constraints. First, support high-concurrency structured data queries. Database connection count configuration must match scenarios where multiple departments call ledgers simultaneously. Second, support storage of unstructured attachments in different formats. File storage services must adapt to batch upload and index generation. Third, support flexible data filtering by batch and time period. Workflow data source configuration must clearly associate field mapping rules for corresponding systems. During upgrades, index data of historical ledgers must be migrated synchronously to avoid invalidation of original data association relationships due to version iterations.
How to set the configurations
| Configuration Item | Suggested Value | Basis for This Setting |
|---|---|---|
MONGODB_URI | mongodb://chem-admin:pass123@db-host:27017/chem_ledger?authSource=admin | Unstructured quality inspection attachments and historical batch data of basic chemical ledgers must be stored using MongoDB. Correct database authentication source and access account must be specified |
STORAGE_S3_ENDPOINT | http://minio.chemical-site.com:9000 | Store unstructured files such as batch-uploaded quality inspection reports and ledger scanned documents. Use S3-compatible local storage services to adapt to internal network environments |
REDIS_URL | redis://redis-pass@redis-host:6379/1 | Cache ledger data for high-frequency queries such as daily feeding summaries and batch qualified statistics to reduce database concurrency pressure |
DB_MAX_LINK | 200–300 | Concurrent queries for basic chemical ledgers come from multiple departments including production scheduling, quality inspection, and warehousing. This connection count range can meet daily business needs |
FE_DOMAIN | https://fastgpt.chemical-enterprise.com | Configure the frontend access domain name to adapt to jump links of internal office systems, ensuring users can normally access the ledger aggregation workflow |
FILE_TOKEN_KEY | Randomly generated 32-bit secure string | Encrypt access tokens for ledger files to prevent unauthorized users from accessing sensitive production and quality inspection data |
Three common mistakes
- Symptom: An uncaught exception occurs when calling the AI chat component. Cause: During private deployment,
MONGODB_URIis not configured with the correct authentication source for ledger data, making it impossible to read structured and unstructured data from historical ledgers. - Symptom: After locally upgrading to FastGPT v4.17.0, the frontend page has style disorder or static resource loading failures. Cause: The reverse proxy configuration corresponding to
FE_DOMAINwas not updated synchronously, causing frontend resources to fail to load normally. - Symptom: The knowledge base search returns "no matching results". Cause:
REDIS_URLis configured incorrectly, causing the ledger cache for high-frequency queries to fail, making it impossible to associate with quality inspection report data for the corresponding batch.
How to confirm the configuration is correct
- Log in to the FastGPT backend system configuration page, and check one by one whether the format and parameters of configuration items such as
MONGODB_URIandSTORAGE_S3_ENDPOINTare correct. - Upload a basic chemical quality inspection report Excel file to verify that the file can be stored normally and the corresponding vector index is generated.
- Initiate a ledger aggregation workflow test to check whether it can correctly pull feeding and quality inspection data for a specified time period and generate a complete report.
- Check that the frontend page loads normally, and that the internal office system jump link points to the configured
FE_DOMAINaddress.
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.