Data Characteristics
Market access product data in the biopharmaceutical sector includes regulatory documents, guidelines, technical review requirements, approval processes, registration application templates, and public information on approved products. These are issued by national or regional drug regulatory agencies. Data sources are typically official websites, databases, or professional consulting reports. Documents often exist as PDFs, Word files, or structured data (e.g., XML). Content covers legal clauses, technical specifications, clinical data summaries, and pharmaceutical research reports. Update frequency depends on policy adjustments and new product launches, usually quarterly or annually. Urgent policy changes may trigger ad-hoc updates. Document structures are complex, containing numerous technical terms, tables, figures, and cross-references. Key fields include drug name, indications, registration classification, approval number, effective date, manufacturer information, and various technical indicators.
Constraints on Knowledge Base Retrieval and Recall
The complexity and high timeliness of market access data impose specific requirements on knowledge base retrieval and recall. Regulatory documents contain many technical terms and acronyms. This requires text understanding models to possess strong domain vocabulary recognition capabilities to avoid semantic drift. Uncertain update frequency means the knowledge base needs to support efficient incremental update mechanisms to ensure the timeliness of retrieval results. Complex internal document structures and cross-references mean simple text segmentation may break context, affecting recall completeness. For example, if a clause references a definition in another document, improper segmentation might recall only the clause, missing the critical definition. The existence of multilingual documents, especially the need to retrieve English literature using Chinese queries, requires cross-language matching capabilities. Furthermore, the rigor of regulatory provisions demands that retrieval results strictly adhere to the original text. The model must not over-summarize or rephrase to avoid misinterpreting policies.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters | Market access document paragraphs are often long, containing multiple regulations or explanations. Longer segment lengths help maintain contextual completeness and avoid splitting critical information. |
Ideal Chunk Length (Ideal Chunk Length) | 600–900 characters | Considering the average length and information density of regulatory clauses, this range ensures chunk content independence while facilitating model comprehension and reducing redundancy. |
Custom Separator (Custom Separators) | \n\n (two newlines) and 。 (Chinese period) | Regulatory documents often use paragraphs or periods as logical separators. Double newlines effectively identify main paragraphs, and periods serve as auxiliary separators for sub-clauses within long sentences. |
Recall count (Number of Retrieved Items) | Top 8–12 items | Market access questions often require information from multiple perspectives or documents. Increasing the number of retrieved items improves the probability of hitting relevant regulations or explanations, covering a more comprehensive context. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Market access questions demand high accuracy. A higher similarity threshold ensures retrieved content is highly relevant to the query intent, reducing interference from irrelevant or ambiguous information. |
Text Understanding Model | Choose a model that supports multiple languages and performs well in legal and regulatory domains | Market access documents involve multiple languages and specialized terminology. A high-performance text understanding model can better parse complex sentences, identify domain entities, and support cross-language matching, improving retrieval accuracy. |
Common Mistakes
- Retrieval results contain irrelevant content or miss critical information. This happens when
Chunk size(Segment Length) is set improperly, leading to related context being cut off or irrelevant content being included. - A user inputs a Chinese query but cannot accurately recall English regulatory documents from the knowledge base. This occurs when the
Text Understanding Modelis not configured or an unsupported cross-language understanding model is selected. - After a knowledge base update, retrieval results do not reflect the latest policy changes. This happens when the knowledge base synchronization mechanism is not triggered promptly or the incremental update strategy does not effectively cover new content.
How to Confirm Correct Configuration
- For typical market access questions, perform a retrieval and check the
Similarity Scoreof the results. Evaluate their relevance to the query and verify if the retrieved segments are complete and unbroken. - Use queries containing mixed Chinese and English content to test whether the system can accurately retrieve relevant documents from both Chinese and English knowledge bases, verifying cross-language retrieval capabilities.
- Upload a document containing the latest policy changes, then perform relevant queries. Confirm that retrieval results reflect the latest regulatory requirements and check the
Update Timestamp.
The values provided are common starting points and should be measured against specific 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.