Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Metal material processing service consultation data mainly comes from internal enterprise process specifications, material sheets, quality inspection reports, and service approval guidelines released by state-owned enterprises and government agencies. Data update rhythm varies with business scenarios: process specifications are updated quarterly with process iterations, quality inspection reports are updated daily with production batches, and service guidelines are updated once every six months with policy adjustments. The document structure is divided into four modules: material grade, processing parameters, quality inspection indicators, and service workflow. Fields include tensile strength (unit: MPa), hardness (unit: HRC), processing temperature (unit: ℃), approval node duration (unit: hours). The length of a single document ranges from hundreds to tens of thousands of characters.
What constraints do these characteristics impose during deployment and upgrade
Metal material processing service consultation data contains a large number of professional parameters and fixed-format process documents. Configure sufficient storage capacity during deployment to adapt to single-batch documents with tens of thousands of characters. Set incremental sync trigger rules for high-frequency data updates to avoid excessive server resource usage from full synchronization runs. Verify embedding model compatibility during upgrades, as professional field embedding requires models that support industrial terminology. Adjust chunking strategies during deployment to enable precise knowledge base chunk matching for service workflow node association, avoiding process logic breaks caused by information splitting.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://admin:securepass@mongodb-host:27017/fastgpt?authSource=admin, paired with DB_MAX_LINK=50 | Metal material processing quality inspection and process data have batch associations. Replica set configuration ensures read/write stability. 50 connections meet the read/write requirements for thousands of daily documents |
REDIS_URL | redis://:redispass@redis-host:6379/0, allocated memory no less than 1000 MB | Caches real-time production data and service sessions. 1000 MB of memory supports hundreds of concurrent consultation requests |
FE_DOMAIN | http://your-server-ip:3000 or a bound official domain | Provides a unified external access entry for service consultations, and adapts to access rules of internal office networks of state-owned enterprises and government agencies |
ROOT_KEY | Randomly generated 32-character string | Ensures the security of FastGPT background management permissions, and prevents unauthorized access to configuration items |
STORAGE_S3_ENDPOINT | Leave blank if using local storage, fill in the endpoint address of the corresponding service provider if using object storage | Adapts to the large-capacity storage requirements of metal material processing documents, and supports batch upload of quality inspection reports and process manuals |
Three Common Mistakes
- Symptom: No response when accessing port 3000 after starting containers, with connection timeout error. Cause: Port mapping is not correctly configured in docker-compose.yml, or the server firewall does not open port 3000, preventing external access to the service consultation entry.
- Symptom: After uploading a PDF related to metal materials, the knowledge base fails to parse the text content. Cause: The pdf-marker plugin is not correctly deployed, or the plugin is not associated with FastGPT's
FILE_TOKEN_KEY, causing the file parsing process to interrupt. - Symptom: Cannot initiate consultation requests after configuring a third-party AI channel. Cause: The channel secret key is filled incorrectly, or
DB_MAX_LINKis set too low, causing the database connection pool to be exhausted and unable to establish API request connections.
How to Confirm Correct Configuration
- Run the
docker compose pscommand, confirm that all container statuses are healthy. - Access the
FE_DOMAINaddress, confirm that the FastGPT login and consultation pages load normally. - Upload a metal material material sheet PDF, confirm that the knowledge base entries can be successfully parsed and generated for retrieval.
- After configuring a third-party AI channel, initiate a test conversation, confirm that AI-generated service consultation replies can be obtained normally.
The values provided are common starting points and should be validated against local sample data.
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.