Vector Model and Indexing for Market Access Registration and

Data Characteristics

Market access registration and declaration documents in the biopharmaceutical sector include drug/device registration certificates, instructions for use, technical review reports, clinical trial data, pharmacoeconomic evaluation reports, domestic and international regulatory policies and guidelines, and market research reports for approved products. Data sources are diverse, including NMPA databases, internal enterprise document systems, third-party research reports, and medical journals. Data update frequencies vary; regulatory documents may be revised annually, while clinical data updates dynamically with trial progress. Document structures are diverse, encompassing both structured table data (e.g., indications, dosages, adverse reactions) and unstructured text content (e.g., safety analysis, mechanism of action descriptions). Fields often involve pharmaceutical, medical, and statistical terminology, such as ATC codes, ICD-10 codes, Cmax, and AUC. Units include mg, mL, μg/kg, and %.

Constraints Imposed by These Characteristics on Vector Models and Indexing

The specialized and diverse nature of market access documents requires vector models to accurately capture the deep semantics of medical terminology, avoiding biases from simple word frequency matching. The iterative nature of regulatory document versions necessitates that the knowledge base supports efficient version management and incremental updates to ensure the timeliness of recall results. The mixture of unstructured text and structured data challenges segmentation strategies; overly long paragraphs dilute key information, while overly short ones may lose context. For example, pharmacoeconomic reports contain numerous charts and complex argumentation processes, requiring more refined segmentation to preserve logical integrity. The presence of specialized fields and units requires vector models to possess domain knowledge to correctly understand parameters like LD50 (median lethal dose) or AUC0-t (area under the curve from time 0 to t) and to perform effective unit conversion and matching during retrieval.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk Size`	`800–1200 characters`	Balances the completeness of regulatory provisions with the logical argumentation of technical reports, preventing key information from being fragmented or redundant.
`Chunk Overlap Size`	`100–150 characters`	Ensures contextual continuity across segments, especially for regulatory clauses and clinical trial conclusions with strong logical connections.
`embedding_model`	`Qwen/Qwen3-Embedding-8B`	This model performs well in the Chinese biopharmaceutical domain, effectively handling specialized terminology and complex sentence structures.
`Recall Count`	`Top 8–12 chunks`	Ensures comprehensive recall while balancing the efficiency of subsequent reranking and LLM processing.
`Similarity Threshold`	`0.75–0.82`	Filters out less relevant results for market access documents, improving recall quality. The specific value requires empirical testing with domain data.
`Rerank Model`	`Qwen/Qwen3-Embedding-8B`	In most scenarios, consistency with the embedding model simplifies deployment and leverages its bidirectional encoding capabilities for reranking.

Three Common Mistakes

Knowledge base query results lack critical regulatory clauses or clinical data. This may occur if the recall count is too low or the similarity threshold is too high, filtering out important but slightly less relevant information.
Uploading large market research reports or technical review reports triggers an UPLOAD_FILE_MAX_SIZE error. This indicates the file size exceeds the limits of FastGPT or the underlying storage service.
RAG question-answering results show factual errors regarding drug dosages or adverse reaction descriptions. This usually results from an improper Chunk Size setting, which separates critical numerical values or qualifying conditions from the descriptive text, preventing the model from acquiring complete information.

How to Confirm Proper Configuration

Select typical registration and declaration scenarios and verify that recall results include all expected original regulatory texts, key paragraphs from technical reports, and clinical data. Check the Similarity score distribution.
Use the knowledge base preview function in the FastGPT backend. Randomly select different document types and check if their segmentation is reasonable, especially for paragraphs containing charts, tables, or complex logical arguments.
Conduct multi-round question-answering tests for key indications and contraindications of specific drugs or devices. Evaluate the accuracy and completeness of the LLM's answers and trace whether its citations are precise to the original paragraphs.

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.