Deployment and Upgrade for Cleaning Validation Registration

Data Characteristics

Cleaning validation registration dossier data primarily originates from production records, analytical reports, method validation documents, risk assessment reports, and deviation handling records. This data updates infrequently, typically with product batches or method changes. Document structures mainly include batch production records, inspection reports, and validation protocols and reports. These are usually in PDF or Word formats, containing numerous tables, chromatograms, and structured text. Fields cover equipment numbers, batch numbers, cleaning agent names, residue limits, detection methods, recovery rates, sample numbers, and test results. Units include ppm, µg/cm², and mg/L, often requiring unit conversions. The data also contains extensive unstructured descriptions, such as cleaning process descriptions and deviation investigation conclusions.

Constraints on "Deployment and Upgrade"

The multi-source and low-update frequency nature of cleaning validation data requires a deployment solution that effectively integrates files from different systems and includes version control capabilities for historical record traceability. The mix of structured and unstructured document characteristics necessitates robust document parsing capabilities during data preprocessing, especially for table and chromatogram recognition. The presence of specialized fields like residue limits and unit conversions places higher demands on FastGPT's knowledge base chunking strategy and recall accuracy. This ensures critical numerical values and units are not incorrectly split or lost. Furthermore, regulatory compliance requires data processing traceability and auditability, making logging and permission management important considerations.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`200 MB`	Cleaning validation reports can contain many images and chromatograms, leading to large file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Parsing large PDF or Word documents can be time-consuming.
`Chunk Length`	`800 characters`	Balances the need for long document context with precise phrase matching.
`Recall Count`	`15 entries`	Ensures critical information is sufficiently covered during recall.
`Similarity Threshold`	`0.75`	Improves the relevance of recall results and reduces noise.
`Rerank Return Count`	`5 entries`	Refines the key information presented to the user.

Common Pitfalls

In a private deployment environment, FastGPT containers fail to correctly configure access to the local ollama service, resulting in model call failures. Common symptoms include connection refused or service unreachable errors in logs. This occurs due to container network isolation, requiring explicit mapping of the ollama service port to the host and specifying the correct ollama service address in the FastGPT configuration file.
Upgrading FastGPT versions by directly running the upgrade script without handling compatibility issues for older configuration files leads to service startup anomalies or missing functionality. Symptoms include the service failing to start after an upgrade or specific functions reporting errors. This happens because some version upgrades introduce new configuration items or modify existing configuration structures.
When using docker build to construct an image, encountering dependency download failures or checksum errors interrupts the image build. Symptoms include ERROR: failed to solve: failed to checksum in the build logs. This is often caused by network environment issues, unstable source servers, or a mismatch between the dependency version specified in the Dockerfile and the actually available downloadable version.

Verification

Upload a cleaning validation report PDF containing tables and specialized units (e.g., µg/cm², ppm). Observe the file parsing progress and knowledge base chunking effect to ensure table content and units are correctly identified and chunked.
Query the uploaded knowledge base about residue limits, detection methods, or equipment numbers. Verify that the recall results include critical numerical values and specialized terms from the document, and evaluate the accuracy of the answers.
Simulate a version upgrade. After following the upgrade documentation steps in a test environment, check that the FastGPT service starts normally and all core functionalities (e.g., knowledge base management, conversational interaction) work correctly without errors.
Review FastGPT's operational logs to confirm no error messages related to ollama service connection appear and that the knowledge base vectorization process completes successfully.

Note: The values provided are common starting points. Measure them against your 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 2026-09-21.