Data Characteristics in This Category
Registration and declaration documents in the biopharmaceutical sector primarily use data from regulatory bodies (e.g., NMPA, FDA) through regulations, guidelines, and technical review reports. They also use internal company data such as R&D reports, clinical trial data, and manufacturing process documents. Update frequencies vary; regulatory documents have defined revision cycles, while internal data generates in real-time with R&D progress. Document structures are complex, often including PDF, Word, and Excel formats. Content covers pharmaceutical research, pharmacology and toxicology, clinical research, and quality management. Fields and units are highly specialized. Examples include content assay (%) and stability studies (months) in pharmaceutical research, pharmacokinetic parameters (e.g., Cmax, Tmax) in pharmacology and toxicology, subject counts (cases) and adverse event rates (%) in clinical trials, and batch numbers and expiration dates.
Constraints on "Citation and Traceability" Due to These Characteristics
The complexity and specialized nature of registration and declaration documents impose strict requirements on citation and traceability. Regulatory documents demand precise citations, down to the version and clause, because any error can lead to declaration failure. Internal R&D data requires systems to differentiate sources to prevent information leakage and ensure traceability to original reports and responsible personnel. Diverse document formats necessitate robust multimodal processing capabilities in RAG systems to extract information accurately. Highly specialized fields and units require models to adhere strictly to industry standards during understanding and generation, preventing ambiguity or misinterpretation. For example, confusing drug concentration units can have serious consequences. Therefore, citation and traceability must ensure accuracy, timeliness, and professionalism, beyond just indicating the source.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
Chunk size | 500–800 characters | Registration documents often contain lengthy discussions; shorter segments can break semantic continuity, while longer ones increase irrelevant information recall. |
Recall count | Top 5–8 entries | Ensures coverage of multiple relevant regulations or internal reports for complex questions, reducing the risk of omissions. |
Similarity threshold | 0.75–0.85 | Registration content is highly specialized, requiring high similarity to ensure precise matching of recalled content and avoid generalization. |
Rerank result count | Top 3 entries | After reranking, the top few results typically have the highest relevance, sufficient to support answer generation. |
maxContext | 8000–12000 token | Registration questions often involve multiple pieces of information, requiring a larger context window to maintain answer completeness. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses parsing of large PDFs or scanned documents, preventing file processing failures due to timeouts. |
Common Pitfalls
- Model responses contain only citation links without specific content. This occurs when segment granularity is too large or the number of recalled items is insufficient to support the model in generating a complete answer.
- Knowledge base citations cannot be selected as variables in code execution nodes. This typically happens when the knowledge base citation feature is not activated in the node configuration or not correctly mapped to available variables.
- Cited regulatory versions in responses do not match the latest release. This is due to the knowledge base not being updated in time or version information not being correctly labeled and processed during document indexing.
How to Verify Configuration
- For typical regulatory questions, check if model responses accurately cite specific regulatory clauses and verify the consistency between the cited text and the original source.
- Select several queries involving internal R&D data. Verify that the data sources cited by the model are traceable to the corresponding internal reports or experimental records.
- Upload a PDF document containing complex tables or charts. Check if the model can correctly extract and cite key field information, verifying its multimodal processing capabilities.
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.