Data Characteristics for this Category
Solid tumor registration and submission documents draw from diverse sources. These include clinical trial reports, non-clinical study reports, pharmaceutical research reports, technical guidelines from regulatory bodies, and public information on approved drugs. Update frequencies vary; clinical trial data may release in stages, while regulatory guidelines revise periodically. Document structures are complex, often in PDF, Word, or structured XML formats. They contain extensive text, tables, and figures. Fields and units are highly specialized. For example, dosage units are commonly mg/kg or mg/m². Pharmacokinetic parameters involve Cmax, AUC, t1/2. Pathology reports include specific metrics like tumor size (mm) and grading (G1-G4).
Constraints on Knowledge Base Retrieval and Recall
The complexity and specialization of solid tumor data impose multiple constraints on knowledge base retrieval and recall. First, document diversity requires the knowledge base to handle various file formats. It must effectively extract key information, especially data nested within tables or figures. Second, the prevalence of specialized terminology and abbreviations demands strong semantic understanding from the model. It needs to accurately identify and link the same concept across different expressions. For instance, PD-1 inhibitor and programmed death receptor 1 inhibitor should be treated as synonyms. Third, inconsistent data update frequencies necessitate support for incremental updates and version management. This ensures recalled information reflects the latest regulatory requirements or clinical data. Finally, the high demand for accuracy and traceability in registration and submission documents means recall results must be relevant and point to the specific location in the original document, facilitating engineer verification.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters | Ensures each segment contains sufficient contextual information. Avoids excessive length, which can lead to information redundancy or reduced model processing efficiency, especially when handling lengthy clinical reports. |
Chunk Overlap Length (Segment Overlap Length) | 100–150 characters | Maintains contextual continuity between segments. Reduces the risk of important information being cut off, particularly for specialized terms or data expressions spanning paragraphs. |
Recall count (Number of Retrieved Items) | 10–15 items | Balances retrieval breadth with processing cost. Ensures coverage of potentially relevant information while avoiding excessive irrelevant noise. Suitable for complex query scenarios. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Requires adjustment based on dataset characteristics and recall quality requirements. An initial setting of 0.75 is a common starting point. Validate recall precision and recall rate using a test set. |
Rerank result count (Number of Reranked Items) | 5 items | Focuses on the most relevant core information for the user query. Improves the quality and efficiency of the final answer presented to the engineer. Reduces manual screening effort. |
embedding_model | text-embedding-ada-002 or higher version | Addresses specialized vocabulary and complex semantic relationships unique to the biomedical field. Enhances the accuracy of vector representations, thereby optimizing similarity calculations. |
Common Pitfalls
Knowledge base retrieval results often have low precision, frequently returning irrelevant document snippets. This typically occurs because the chunking strategy is too coarse, failing to effectively preserve contextual connections between specialized terms or data.
The Knowledge Base Selection module in the workflow may not function as expected, leading to an overly broad retrieval scope or omission of specific knowledge bases. This is often due to spelling errors or incorrect assignment of Knowledge Base Name or Knowledge base ID (Knowledge Base ID) during configuration.
Retrieval time significantly increases, or retrieval timeout errors occur. This can relate to setting the number of retrieved items too high or insufficient vector database index optimization, especially when processing a large volume of documents.
How to Verify Configuration
Execute a series of queries containing solid tumor-specific terminology and key data (e.g., EGFR mutation, objective response rate, adverse event grade). Check if retrieval results include relevant document snippets and can pinpoint specific sections of the original material.
Validate Recall count (Number of Retrieved Items) and Similarity threshold (Similarity Threshold) configurations. Simulate questions from a registration and submission document reviewer. Observe if the returned results cover all key information points required to answer the questions and assess the reasonableness of their ranking.
Conduct performance tests. Record retrieval response times under different query loads. Confirm that system response speed meets daily usage requirements after configuring Recall count (Number of Retrieved Items) and Rerank result count (Number of Reranked Items), avoiding retrieval timeout phenomena.
After regularly updating knowledge base content, perform verification queries. Ensure newly added or modified materials are correctly indexed and recalled, especially for the latest regulatory guidelines or clinical trial data.
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.