What this category of data looks like
Typical data sources for this category include cell banks and seed lots, process analysis systems, cleanroom monitoring equipment, MES, LIMS, and equipment integrity testing tools. Data update cadences include batch production record updates, real-time environmental monitoring data uploads, and test reports generated per fixed monitoring plans. Daily documents include cell bank or seed lot records, culture and purification records, aseptic filling records, environmental monitoring reports, media fill simulation records, sterility test reports, and batch release documents. Fields include batch number, culture temperature, cleanroom settleable bacteria count, equipment runtime, and corresponding units such as ℃, cfu/m³, hours, and others.
What constraints these characteristics impose during deployment and upgrade
Multiple dispersed data sources require adapting deployments to different system interface formats to avoid data parsing failures. Real-time and batch update cadences require configuring processes that support incremental retrieval and bulk updates to ensure data timeliness. Document structures include both structured LIMS data and unstructured paper scan records, requiring adaptation to different chunking and parsing rules. Additionally, data errors can cause severe security risks. Deployment must include data integrity check configurations to prevent contaminated or invalid data from entering the knowledge base.
How to set configurations
This configuration applies to FastGPT v4.17.0, with details as follows:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://[username:password@]host:port/fastgpt_bio | Stores structured batch release documents, LIMS data, and process specification metadata |
REDIS_URL | redis://:[password]@host:port/1 | Caches real-time cleanroom monitoring data and chat session contexts to speed up retrieval |
STORAGE_S3_ENDPOINT | http://internal-s3-endpoint:9000 | Connects to internal object storage for bulk storage of unstructured process record documents |
DB_MAX_LINK | 100–200 concurrent connections | Adapts to concurrent processing needs for multi-batch process data, preventing database connection exhaustion |
FILE_TOKEN_KEY | 32-bit randomly generated string | Secures permissions for internal file upload and access, preventing unauthorized calls |
ROOT_KEY | Secret string only known to internal operations staff | Manages highest-privilege access to the FastGPT platform, ensuring deployment security |
Three common mistakes
- Uploading an aseptic filling record document after local deployment results in empty data processing display. The cause is that
STORAGE_S3_ENDPOINTwas not correctly configured to point to the internal object storage, causing uploaded documents to fail normal parsing and storage. - After running a Docker-packaged image, all vector search results have the same score. The cause is that
REDIS_URLwas not configured or was configured incorrectly, causing the caching mechanism to fail. Vector search does not trigger incremental updates, always returning results from the initial dataset. - After calling a local model, the first response works normally, but subsequent requests with historical context trigger errors. The cause is that
DB_MAX_LINKwas not adjusted. Database connections are occupied by the first request and not released, preventing loading of historical session data.
How to confirm configurations are correct
- Upload a media fill simulation record document, check background task logs and the knowledge base list to confirm the document has completed parsing and vector generation, with no error messages.
- Initiate two consecutive cleanroom settleable bacteria count queries, check if returned results include the latest monitoring data to confirm caching and retrieval processes are working properly.
- View the system background connection monitoring panel to confirm that connections for
MONGODB_URIandREDIS_URLare normal, with no timeout or disconnection errors. - Create a knowledge base containing more than 3 batches of process data, check the completion status of bulk processing tasks to confirm that the
DB_MAX_LINKconcurrency configuration adapts to bulk processing needs.
The values provided are common starting points and should be measured against the reader's own samples.