Data Characteristics in this Category
Biopharmaceutical regulatory submission data primarily originates from public documents released by regulatory bodies such as the National Medical Products Administration (NMPA) and the U.S. Food and Drug Administration (FDA), as well as internal company submission materials. This data has a relatively low update frequency, typically changing with new product launches or regulatory revisions, rather than being real-time dynamic. Document structures are complex, including various technical review reports, clinical trial data, manufacturing process information, quality standards, and instructions. These are often presented in formats like PDF, Word, and Excel. Fields and units are highly specialized; for example, "active ingredient content" is typically expressed in mg/tablets or U/ml, "stability study period" in months or years, and "mechanism of action" as descriptive text. The data often contains numerous tables and graphs, and submission templates and requirements vary across different regulatory agencies.
Constraints Imposed by these Characteristics on "Forms and Interactions"
The complexity and specialized nature of regulatory submission data impose specific requirements on form design and user interaction. First, the authority and update frequency of data sources dictate that knowledge base construction must prioritize data source reliability and perform incremental updates regularly. Second, the diversity of document structures requires forms to handle different file types, such as OCR recognition for PDF files and extraction of key information from complex Word documents. The specialized nature of fields and the strictness of units mean that form input must provide precise unit selection or validation to prevent incorrect entries. For descriptive text, such as "mechanism of action," support for long text input and semantic understanding is necessary. Additionally, since the submission process involves multiple stages and numerous fields, interaction design must be clear, guiding users through information entry step-by-step, and capable of handling logical relationships and validation between data to ensure the completeness and accuracy of submitted information.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size | 500-800 characters | Regulatory submission documents often contain long descriptive paragraphs. This length helps maintain semantic integrity while preventing individual segments from becoming too large. |
Overlap Length | 50-100 characters | Ensures contextual continuity between segments, especially when processing complex technical descriptions. |
Recall count | 8-12 entries | Regulatory submission questions typically require more contextual information. Increasing the number of recalled items can improve relevance. |
Similarity threshold | 0.75-0.85 | Ensures recalled results are highly relevant to the user's query, avoiding the introduction of inaccurate technical information. |
maxContext | 3000-4000 token | Specialized regulatory submission questions often require a longer context for accurate answers. This range can accommodate more information. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing regulatory submission files (e.g., large PDFs) can be time-consuming. Increasing the timeout prevents parsing failures. |
Three Common Pitfalls
- After a user uploads an Excel file, RAG retrieval results are disordered or inaccurate. This occurs because Excel files often have complex multi-column structures, and automatic segmentation may fail to correctly identify each row as an independent logical unit.
- User selection or form input components set in a workflow do not trigger user interaction prompts when accessed via API in the publishing channel. This happens because API interfaces typically expect structured data by default and lack additional configuration for interactive components, preventing the platform from recognizing and returning corresponding interaction instructions.
- An error
undefined model must match "^(text |occurs when selecting an embedding model other thantext-embedding-ada-002for the knowledge base. This is because the platform defaults to or recommends specific embedding models. Switching to other models requires ensuring their compatibility with FastGPT or performing additional model configurations.
How to Confirm Correct Configuration
- Upload a representative regulatory submission document (e.g., a drug's instructions in PDF format) and check the knowledge base segment preview. Confirm that each segment contains complete logical information, such as a full description of the mechanism of action or a complete section of clinical trial data.
- Simulate user queries (e.g., "What are the indications for drug X?", "What is the active ingredient content in submission material Y?"). Check if the returned results accurately recall relevant content from the knowledge base and correctly identify and extract field values.
- Call a workflow with form input via an API interface. Check if the API response includes the expected form field definitions and if the workflow executes as expected and returns results after valid input is provided.
Note: The values provided are common starting points. It is recommended to measure against your own samples for optimal performance.
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.