Data Characteristics for this Category
Registration and declaration documents in the biopharmaceutical field draw from diverse sources and data types. These include clinical trial reports, non-clinical study reports, manufacturing process documents, quality standards, stability study data, and product specifications. These documents typically exist in formats like PDF, Word, and Excel. They contain extensive specialized terminology, abbreviations, charts, and data tables. Data update frequency depends on R&D progress and regulatory requirements; for example, clinical trial data updates periodically, and regulatory changes can lead to document revisions. Document structure often adheres to fixed templates from regulatory bodies, featuring strict hierarchical relationships and section numbering. Fields and units involve dosages (mg/kg), concentrations (μg/mL), time (h, day), and statistical indicators (p-value, confidence intervals). The accuracy and consistency of units are critical.
Constraints from these Characteristics on "Deployment and Upgrade"
The complexity of registration and declaration documents imposes specific deployment and upgrade requirements. First, the specialized and structured nature of these documents requires the knowledge base to recognize and maintain contextual integrity during document chunking, preventing the fragmentation of critical information. For example, the association between dosage groups and observation indicators in clinical trial reports needs special handling. Second, the cyclical nature of data updates means the system must support incremental updates and version management. This ensures the knowledge base always reflects the latest state while allowing historical versions to be traced. Third, the numerous tables and charts in documents challenge file parsing capabilities. The system must effectively extract and index this non-textual information. Finally, the strict requirements for fields and units dictate that numerical values and units must be accurately presented during knowledge extraction and answer generation to avoid misinterpretation or confusion. This directly impacts the setting of the model's maxContext and chunking strategy.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8192 | Registration and declaration documents have strong contextual relevance, requiring a longer dialogue history and reference content. |
Chunk size (Chunk Length) | 800–1200 characters | Balances information completeness and retrieval efficiency, preventing semantic loss due to splitting. |
Recall count (Recall Count) | Top 8 entries (Top 8) | Ensures coverage of multi-source document information, especially when cross-referencing regulatory clauses and experimental data. |
Similarity threshold (Similarity Threshold) | 0.75 | Medical terminology requires high precision; a lower threshold can introduce irrelevant content. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing large PDF files (e.g., clinical trial reports) can require significant parsing time. |
UPLOAD_FILE_MAX_SIZE | 200 MB | Accommodates declaration documents containing numerous charts and high-resolution images. |
Three Common Mistakes
- Model returns abnormally long or truncated content after deployment: This often occurs when the language model's
maxContextparameter does not match the FastGPT's internal context window configuration, causing the model to operate under a smaller actual limit. - File parsing service continuously reports errors during local deployment: This is typically due to missing necessary dependency libraries or incorrect environment variable configurations, preventing the file parsing process from starting correctly.
- Table data in retrieval results is not effectively presented: The file parser fails to correctly identify and extract table structures from PDFs, leading to table content being treated as plain text or directly lost.
How to Confirm Correct Configuration
- Upload a PDF clinical trial report containing complex tables and charts. Check if the knowledge base can correctly parse and generate a preview.
- Query a Word document with specific regulatory clauses. Verify if the model can accurately cite the original passages and provide correct answers.
- Simulate an incremental knowledge base update. Upload a new version of a product specification. Confirm that the knowledge base content has synchronized and that historical versions are traceable.
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 2026-09-21.