Data Characteristics
GMP-compliant data in biopharmaceuticals primarily originates from batch records, inspection reports, deviation management, change control, and supplier qualification documents. These documents are typically in PDF, Word, or Excel formats. Some highly digitized companies may integrate them into LIMS (Laboratory Information Management Systems) or QMS (Quality Management Systems). Data update frequency correlates closely with production batches and quality events. New data may appear daily, but core procedural documents (e.g., SOPs) update less frequently, usually annually or in response to regulatory changes. Document structures are highly standardized, with clear section headings, tables, and attachments. Fields and units strictly follow industry norms, such as batch numbers, production dates, expiry dates, inspection results (e.g., percentage content, impurity limits), and instrument calibration data (e.g., temperature in ℃, pressure in MPa). Precision and traceability requirements are extremely high.
Deployment and Upgrade Constraints
Highly standardized and structured GMP-compliant data requires a refined document parsing strategy for knowledge base construction. The large volume of PDF batch records and reports means FastGPT's file parsing service must reliably process large, multi-page PDF files during deployment, accurately extracting tabular data and key fields. Varying data update frequencies impact the knowledge base's indexing update mechanism. Low-frequency documents like SOPs can use periodic full or incremental updates. High-frequency data like batch records require near real-time data ingestion and index updates. Strict field and unit adherence demands that FastGPT's retrieval and Q&A functions precisely differentiate fields at the semantic understanding level to avoid confusion. For example, it must correctly link batch numbers with production dates and compare inspection results against corresponding testing standards. Furthermore, data traceability requirements mean the knowledge base must provide original document links or citations in retrieval results.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Individual GMP batch records or inspection reports can contain numerous attachments and images, resulting in large file sizes. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing complex PDF documents, especially those with embedded images and tables, can be time-consuming. |
Chunk size (Segment Length) | 800 characters | GMP document paragraphs are logically dense; overly short segments may cut off critical information, affecting semantic completeness. |
Recall count (Recall Count) | 10 entries | Increases the initial recall scope, ensuring coverage of multiple relevant batch records or procedures. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Requires balancing recall and precision; fine-tune using a test set in actual business scenarios. |
Rerank result count (Rerank Return Count) | 5 entries | Ensures the final results returned to the user focus on the most relevant few items, avoiding information overload. |
Common Pitfalls
- After FastGPT deployment, the OneAPI service repeatedly restarts or fails to start, with error messages indicating port conflicts or insufficient memory. This typically results from overly low resource limits for the OneAPI container in the
docker-compose.ymlconfiguration, or other services on the host machine using the same ports. - When uploading a large batch of PDF batch record files, file uploads succeed but the knowledge base content is empty or parsing fails, with logs showing
PARSE_FILE_TIMEOUT. This occurs because the default file parsing timeout is insufficient for large PDF documents containing many tables and images. - When users ask about inspection results for a specific batch product, the AI answer shows data confusion, mixing up inspection values from different batches or units for different metrics. The primary reason is that the knowledge base failed to effectively identify and isolate key fields during document segmentation, leading to a lack of contextual precision in retrieval results.
Verification Steps
- Upload a PDF batch record containing complex tables and multiple pages. Confirm the file parses correctly and that tabular data and key fields are fully retrievable from the knowledge base.
- Ask about operational steps from a recently updated SOP document. Verify the accuracy of information in the AI's answer and confirm that cited knowledge points trace back to the latest version of that document.
- Randomly select inspection reports from multiple batches. Ask questions about different batch numbers and inspection items. Confirm the AI can precisely differentiate batch information and provide inspection results with correct units.
- Simulate high-concurrency file uploads. Observe FastGPT's backend file queue processing to ensure large numbers of files are stably received and parsed without significant delays or failures.
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.