What the data for this category looks like
Documents and materials in the coal chemical industry mainly originate from production shift daily reports, equipment maintenance records, process operation specifications, environmental impact assessment approval documents, and safety compliance ledgers. Data update cadence varies by business scenario: daily production records are updated daily, process specifications are revised quarterly, and compliance documents are reviewed annually. Most documents use a chapter-based structure, including fields such as equipment number, material name, operating pressure, and maintenance cycle. Units mostly follow industrial general measurement standards including MPa, t/h, ℃, and other similar metrics. The length of individual documents varies widely, ranging from hundreds-of-word maintenance work orders to tens-of-thousand-word environmental impact assessment reports.
What constraints these characteristics impose on deployment and upgrade
The wide range of document lengths for coal chemical materials requires the knowledge base to support large file chunking and long context processing. Multiple fields and industrial units require retrieval to preserve field semantics and unit matching logic. High-frequency updates of daily production records require the vector database to support incremental updates without full reconstruction. Most coal chemical projects are deployed in intranet environments, so offline model and vector database resources must be supported, with no reliance on public network mirrors or third-party services. These constraints directly determine parameter configuration during deployment and compatibility logic during upgrades, to avoid reduced retrieval accuracy or service interruptions.
How to set configurations
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
REDIS_URL | redis://intranet Redis service address:6379/0 | Adapts to the intranet deployment scenario of coal chemical projects, uses the default 6379 port, database 0 stores session and cache data |
PG_URL | postgresql://username:password@intranet PostgreSQL address:5432/fastgpt?sslmode=disable | Disables SSL connections in intranet environments, uses the default 5432 port, dedicated database stores vector and business data |
DB_MAX_LINK | 20–30 | The wide range of coal chemical document lengths requires controlling concurrent connections within this range to avoid database resource exhaustion; specific values are determined based on actual testing |
STORAGE_S3_ENDPOINT | intranet object storage address:9000 | Uses local object storage services in intranet scenarios, default 9000 port stores uploaded coal chemical documents |
ROOT_KEY | Randomly generated 32-bit high-strength string | Used for admin backend permission verification, ensuring access security for intranet deployments |
FILE_TOKEN_KEY | Randomly generated 16-bit string | Used for signature verification during document upload, preventing unauthorized upload of coal chemical documents |
Three common mistakes
- Symptom: An error occurs when running docker-compose up -d, indicating failure to pull the redis image and unable to connect to the public network image repository. Cause: Most coal chemical projects are deployed in intranets, and no intranet image source is configured or no intranet redis image address is specified, resulting in failure to pull dependent images.
- Symptom: After upgrading the pgvector plugin, vector retrieval functions report errors or return abnormal results. Cause: The pgvector version compatible with the current FastGPT version is not used, and relevant parameters in the
PG_URLdatabase connection configuration are not updated synchronously. - Symptom: When adding coal chemical-specific documents, the system prompts that the knowledge base count has reached the upper limit, and new knowledge bases cannot be added. Cause: The default knowledge base count limit configuration of the open-source version is not adjusted, and the upper limit threshold is not modified through valid parameters.
How to confirm successful configuration
- Log in to the intranet FastGPT admin backend, check the Redis connection status, confirm that it displays normal. The specific connection status is subject to real-time indicators displayed in the backend.
- Run the database connection test script to verify that PostgreSQL and the vector database connections are normal. Confirm that the test results meet business expectations.
- Upload a coal chemical maintenance work order document, confirm that it can be stored normally and vector indexing is completed, and that relevant fields can be matched during retrieval.
- View docker container runtime logs, confirm that there are no error messages related to image pulling or database connections.
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.