Data Profile for This Category
The data for this category primarily comes from formula systems, raw material batch ledgers, internal mixers, Mooney viscometers and rheometers, vulcanizing machines, and laboratory testing equipment. Daily generated documents include formula sheets, weighing records, mixing curves, rubber compound quick test reports, vulcanization process cards, mold usage records, and finished product performance test reports. Data is updated for each raw material truck batch, each rubber compound batch, and each product mold cycle, creating new records each time. Each individual record includes fields such as batch numbers, process parameter values, and test results, and links full-process information between raw materials and finished products.
Constraints Imposed on Deployment and Upgrade Phase
This category has data that updates at high frequency, exists in multiple types, and is closely interconnected. This imposes multiple constraints on the deployment and upgrade phase. High-frequency data writes per truck batch, per rubber compound batch, and per mold cycle require storage components configured to support high concurrent connections, to avoid index lag or write failures. For scenarios with mixed multiple document types, pre-configured file parsing adaptation rules are needed to ensure unstructured data such as mixing curve charts and process card PDFs can be correctly parsed and indexed. The strong data linking characteristic requires that associated fields such as batch numbers be retained in the vector database configuration, to avoid losing process traceability information during subsequent audits. Each individual record includes specific process parameters, so field metadata must be retained during indexing to avoid missing key business information.
Configuration Settings
This document is written based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 50–80 connections | Matches the concurrent demand of high-frequency data writes for this category, and prevents database connection pool exhaustion that causes index failures |
MONGODB_URI | Use a dedicated replica set address | Ensures persistent storage and stable reading of full-process traceability data, and adapts to multi-document associated query requirements |
REDIS_URL | Deploy node local cache instance address | Temporarily stores intermediate data such as batch verification and channel tokens, and reduces high-frequency query pressure on the vector database |
STORAGE_S3_ENDPOINT | Object storage endpoint in the same region as the deployment node | Accelerates upload and reading speeds for multiple types of documents such as mixing curve images and process card PDFs |
FILE_TOKEN_KEY | 32-character random string | Ensures access security of the file upload interface, and prevents unauthorized submission of business documents |
ROOT_KEY | Configure according to internal enterprise permission specifications | Controls platform super administrator permissions, and avoids accidental modification of core audit process configurations |
Common Misconfigurations
- Symptom: Knowledge base indexing gets stuck at high progress without response, and logs show database connection timeout. Cause: The
DB_MAX_LINKconfiguration was not adjusted for the high-frequency data scenario, leading to connection pool exhaustion that cannot handle batch indexing requests. - Symptom: External large model channel test passes after being added, but the channel shows as unavailable when called during the audit process. Cause:
REDIS_URLwas not configured as a cache middleware, leading to failure of temporary storage of channel tokens, and inability to complete cross-request identity verification. - Symptom: After uploading a vulcanization process card PDF, the extracted text lacks core fields such as mold number and vulcanization temperature. Cause: File parsing adaptation rules were not pre-configured, and the field extraction logic for PDF tables was not specified, leading to loss of structured business information.
How to Verify Successful Configuration
- Execute a batch indexing task for business documents of the same category, observe database connection number monitoring, and confirm that the connection number does not reach the configured
DB_MAX_LINKupper limit. - Call the external large model channel test interface, verify that the temporary cache of tokens can be read and updated normally, and confirm that the
REDIS_URLconfiguration is valid. - Upload a business document containing structured tables, check whether the extracted text includes preset core business fields, and confirm that the parsing rules take effect.
- Access the configured frontend access domain name, verify that the platform's login and document upload functions can be used normally, and confirm that the frontend configuration is correct.
The values provided are common starting points and should be measured against the reader's own samples.