Model Access and Configuration for Academic Promotion

Data Characteristics for this Category

Academic promotion registration data involves extensive clinical trial reports, product manuals, user guides, technical specifications, regulatory documents, and market research reports for medical devices, pharmaceuticals, or diagnostic reagents. This data primarily consists of PDF and Word documents, containing numerous specialized terms, statistical data, and charts. Data sources often include internal R&D departments, clinical research organizations, CRO companies, and official bodies like the National Medical Products Administration. Update frequency depends on product lifecycles and regulatory changes; new product development stages may see weekly updates, while marketed products are revised annually or periodically based on regulatory requirements. Document structures are complex, often including multi-level headings, cross-references, and attachments. Fields such as product model, indications, adverse reactions, dosage, administration route, and clinical endpoints have strict specifications and unit requirements.

Constraints Imposed by these Characteristics on "Model Access and Configuration"

The specialized and complex nature of academic promotion materials places specific demands on model access and configuration. First, complex document structures require robust document parsing capabilities to ensure multi-level headings and cross-references are correctly identified, preventing information loss. Second, the large volume of specialized terms and statistical data requires the model to have precise semantic understanding; general-purpose models may struggle to capture subtle meanings. High update frequency necessitates efficient incremental update mechanisms and version management for the knowledge base, ensuring the model always responds based on the latest information. The standardization of specialized fields emphasizes the accuracy of information extraction; model output must strictly adhere to medical and regulatory standards, for example, dosage units like mg/kg, %, and clinical trial phases like IPhase, IIIPhase. This dictates that model configurations for vectorization, retrieval, and generation stages need targeted optimization to balance retrieval precision with content rigor.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Length)	`500–800 characters` (characters)	Balances the completeness of specialized term context with vectorization efficiency, avoiding dilution of key information in long paragraphs.
`Chunk Overlap Length` (Chunk Overlap Length)	`100–150 characters` (characters)	Ensures continuity of information across chunks, capturing semantic connections between adjacent paragraphs.
`Recall count` (Recall Count)	`10–15 entries` (items)	Considers the depth and breadth of specialized materials, increasing recall to cover more relevant information.
`Similarity threshold` (Similarity Threshold)	`0.78–0.85`	Ensures the professional relevance of recalled content, preventing interference from non-core or generic information.
`Rerank result count` (Rerank Return Count)	`3–5 entries` (items)	Selects the most core and direct supporting evidence while maintaining relevance.
`embeddingModel`	`text-embedding-ada-002` or `bge-large-zh`	Stronger semantic understanding for Chinese medical texts, better at capturing specialized vocabulary.

Three Common Pitfalls

Symptom: After uploading PDF or Word documents, model responses lack key information or contain garbled text. Reason: Document parsers have insufficient support for complex tables, embedded text within charts, or specific fonts, leading to incomplete information extraction.
Symptom: Model responses misunderstand specialized terms or confuse numerical units, such as mg and g. Reason: The vector database lacks sufficient context for specialized terms, or the embeddingModel fails to effectively distinguish subtle semantic differences.
Symptom: FastGPT <think></think> tags are empty, and the model cannot perform reasoning. Reason: The large model (e.g., DeepSeek-r1) integrated does not return responses in the format expected by FastGPT under certain request parameters, leading to parsing failure.

How to Verify Correct Configuration

Upload a PDF document containing complex tables and specialized terms. Check if the chunked content in the knowledge base is complete, without garbled text or truncation.
Ask specific professional questions based on the document. Verify if the model's response accurately cites specialized terms and numerical values from the original text, and check if the cited Document ID is correct.
Simulate questions involving comparisons of different product models and indications. Evaluate if the model can accurately distinguish and provide differentiated information, confirming the Similarity threshold (Similarity Threshold) setting is appropriate.

Note: The values provided are common starting points. Measure them against specific samples to determine optimal settings.

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.