Data Characteristics
Talent report data in the biopharmaceutical sector comes from internal HR systems, external recruitment platforms, industry conference records, and expert interviews. Data updates typically occur quarterly or semi-annually, with key executive or core researcher information potentially updated monthly. Document structures are primarily unstructured text, including resumes, project experience, research achievements, and patent lists. Structured fields such as name, title, department, start date, education, and professional skill tags are also present. Units mainly involve time periods (years, months), quantities (number of papers, patents), and seniority levels. Specialized terms and abbreviations (e.g., disease codes, drug molecular formulas, experimental method acronyms) appear frequently in reports.
Constraints on Knowledge Base Retrieval and Recall
The unstructured text content of talent reports, such as project experience and research achievement descriptions, requires the knowledge base to have strong semantic understanding capabilities. This ensures accurate identification of synonymous concepts expressed differently. The presence of structured fields requires the retrieval system to effectively combine keyword matching with vector retrieval for multi-dimensional filtering. Specialized terms and abbreviations in reports demand higher requirements for tokenizers and stop word lists. These need customization or expansion for the biopharmaceutical domain to avoid misinterpreting them as irrelevant words.
Data update frequency is relatively low. Therefore, knowledge base index rebuilding and incremental update strategies must align with this pace to avoid resource waste from frequent operations. Sensitive information in reports, such as salary or personal health status, requires additional access control or anonymization during recall to ensure data compliance.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Size) | 500–800 characters | Ensures individual chunks contain sufficient context while avoiding excessive length that increases noise. Balances semantic completeness. |
Chunk Overlap | 100–150 characters | Guarantees contextual continuity between chunks, improving the recall probability of information spanning multiple chunks. |
Recall count (Recall Count) | Top 5–8 entries | Balances retrieval efficiency and result coverage, ensuring recalled results include enough relevant talent information. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Balances recall precision and recall rate, reducing interference from irrelevant information while not missing potentially relevant reports. |
Rerank result count (Reranked Return Count) | Top 3 entries | Further refines recall results, placing the most relevant few reports at the forefront to enhance user experience. |
Custom Separator (Custom Separators) | 。 ； \n | Effectively splits different statements or paragraphs in reports, preventing multiple pieces of information from being conflated within a single chunk. |
Common Mistakes
- Query results contain information about multiple irrelevant individuals. This typically occurs when the
Similarity threshold(Similarity Threshold) is set too low, leading to the recall of many overly generalized chunks. - After importing Excel talent reports, the system automatically splits them, and a single record contains multiple lines of content. This indicates that
Custom Separator(Custom Separators) are not configured correctly, failing to recognize line breaks or specific symbols within Excel cells. - Queries for specific professional skills occasionally fail to recall relevant reports. This may be due to the tokenizer failing to correctly identify the professional term, or the term not being included in the knowledge base's synonym system.
How to Verify Configuration
- Select 10-15 test questions covering various professional fields and skills. Manually check if the recalled results include at least one highly relevant report and assess its accuracy.
- Query common abbreviations and specialized terms from the reports. Confirm that the recalled results accurately match reports containing these terms and evaluate the relevance of the recalled reports.
- Simulate queries for personnel reports with different titles and departments. Verify that the knowledge base can effectively filter and recall based on structured fields, and check if the number of recalled reports meets expectations.
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.