Data Characteristics in this Category
Market access data in the biopharmaceutical sector originates from official approvals, marketing authorization documents, medical insurance catalogs, pharmacoeconomic evaluation reports from national drug regulatory agencies, and industry guidelines. These documents vary in structure. They include structured tabular data (e.g., indications, dosage forms, prices, reimbursement ratios) and unstructured text (e.g., clinical trial data summaries, approval opinions, pharmacovigilance requirements).
Data update frequencies vary. New drug approvals, medical insurance negotiations, and policy adjustments cause some data changes. Updates typically occur quarterly or annually in batches, but critical policy changes can trigger immediate updates. Fields include generic drug names, brand names, ATC classification codes, registration certificate numbers, approval dates, validity periods, manufacturers, importing countries, medical insurance payment standards, and restricted medication conditions. Some fields may contain multilingual descriptions or specific regulatory terms.
Constraints from these Characteristics on Vector Models and Indexing
The diversity of market access documents requires flexible text splitting strategies for vector models and indexing. Tabular data from approval documents needs structured processing before effective embedding. This avoids loss of contextual relationships from simple text splitting. Unstructured text content requires finer text preprocessing to remove redundant information and standardize terminology.
Varying update frequencies demand efficient incremental update capabilities for the index. This ensures new policies or drug information reflect promptly in search results. Specific regulatory terms and multilingual descriptions require embedding models with strong domain knowledge understanding or multilingual processing capabilities. Furthermore, market access information involves strict compliance. The accuracy and traceability of search results are critical. The index must precisely point to original sources and support filtering and sorting based on specific fields.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters | Balances the integrity of regulatory text with model processing capacity, preventing context fragmentation. |
Chunk overlap (Chunk Overlap) | 100–200 characters | Ensures continuity of information across chunks, improving retrieval recall. |
Recall count (Recall Count) | Top 5–7 items | Balances retrieval efficiency with information coverage, ensuring no critical information is missed. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Adjust via test sets according to business requirements for accuracy and recall. |
Rerank result count (Rerank Return Count) | Top 3 items | Further refines results, prioritizing the most relevant key documents. |
embedding_model | Doubao-embedding v3.0 | Supports Chinese and has good understanding of specific domain terminology. |
Common Pitfalls
- The knowledge base returns information unrelated to the query. This may be due to an unreasonable chunking strategy, leading to incomplete semantic units or excessive noise within a single chunk, affecting vector generation quality.
- The index status remains "Not Ready" for an extended period. This could be due to file parsing or vector generation task timeouts. Check the
PARSE_FILE_TIMEOUT_SECONDSparameter and theUPLOAD_FILE_MAX_SIZEfile size limit. - Structured data, such as medical insurance payment standards, is not effectively utilized in query results. This occurs when tabular data is not properly preprocessed and is directly embedded as text, leading to information loss.
How to Verify Configuration
- Upload a batch of typical market access approval documents. Observe the number of chunks and chunk content for each document in the knowledge base. Ensure critical information is not truncated or excessively diluted.
- Pose questions related to different types of market access issues. Check if the recall results include highly relevant original document segments. Evaluate if the number of recalled items meets expectations.
- Verify the index status. Ensure all uploaded documents have successfully completed vectorization and are not stuck in a "Not Ready" state for too long.
- Test queries involving specific fields (e.g., registration certificate numbers, medical insurance payment standards). Confirm that this field information is accurately identified and cited in the retrieval results.
Note: The values provided are common starting points. Measure them 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.