Data Characteristics
Bispecific antibody regulations and SOP documents originate from pharmaceutical companies' R&D, production, quality control, and clinical departments, as well as regulatory guidelines. These documents update infrequently, mainly during new drug development, production process changes, clinical trial protocol adjustments, or regulatory updates. Document formats vary, including PDF quality standards, Word production batch record templates, Excel stability data sheets, and plain text internal operating procedures. Content often involves complex biomolecular structure descriptions, cell culture conditions, purification steps, quality control indicators (e.g., titer, purity, aggregate content), and corresponding units (e.g., nM, pg/mL, %). Documents may also contain numerous charts, tables, and flowcharts.
Constraints Imposed by These Characteristics on "Deployment and Upgrade"
The complexity and multi-modality of bispecific antibody regulatory documents demand high file parsing capabilities from FastGPT during deployment. PDF-embedded tables and charts, in particular, require efficient OCR and layout analysis for accurate text extraction. Low document update frequency means a potentially large initial data import, but less pressure for subsequent incremental updates. Complex technical terms and units require the vector model to accurately understand their semantics, avoiding misinterpretations due to missing context. Flowcharts and structured data within documents require FastGPT to maintain logical integrity during chunking and retrieval, ensuring accuracy and coherence in question-answering results. When deploying on ARM architecture servers, all dependent components, especially the rerank model, must have ARM-compatible versions.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Regulatory and SOP documents often contain many charts, making individual files large. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Complex PDFs and documents with charts take longer to parse. |
Chunk size (Chunk Length) | 800–1200 characters (characters) | Ensures completeness of professional terms and logical units, reducing semantic loss from chunking. |
Recall count (Recall Count) | Top 10 entries (top 10) | Expands recall scope, covering more potentially relevant regulatory details. |
Similarity threshold (Similarity Threshold) | 0.75 | Balances recall and precision, addressing semantic matching challenges for technical terms. |
Rerank result count (Rerank Return Count) | Top 5 entries (top 5) | Further refines results, focusing on the most relevant regulatory clauses. |
Three Common Mistakes
- Docker image pull failures, displaying authentication errors or connection timeouts, typically result from incorrect proxy configuration of the Docker daemon or invalid mirror source settings.
- After system deployment, some document content parses abnormally, such as missing table data or unidentifiable chart text. This may be due to the file parsing service lacking support for complex PDF layouts or specific OCR engines.
- Misinterpretation of technical terms or incorrect unit conversions in question-answering results indicates that the embedding model in the vector database was not sufficiently trained to cover specific vocabulary and context in the biomedical field.
How to Confirm Correct Setup
- Upload a PDF document containing complex tables and flowcharts. Verify that its content is parsed completely and accurately, paying special attention to the structured extraction of table data.
- Ask questions about a specific production step or quality control standard for bispecific antibodies. Check if the AI's answer references the correct regulatory clauses and numerical values.
- Simulate inquiries about regulatory updates or batch record changes. Observe if the system can update the knowledge base promptly and provide answers based on the latest documents.
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.