Database and Operations for Solid Tumor R&D Document

Data Characteristics

Solid tumor R&D documents cover data from basic research to clinical trials. Data sources are diverse, including pathology reports, gene sequencing data, drug mechanism of action studies, preclinical animal model data, Phase I/II/III clinical trial protocols and results, adverse event records, and drug approval submissions. These documents update relatively slowly, typically at project milestones or regulatory submission points. However, individual documents may contain frequent data revisions. Document formats vary, commonly including PDF for research papers, Word for clinical trial reports, Excel for experimental data tables, and XML or JSON files exported from proprietary databases. Specific fields include tumor staging (e.g., TNM staging), gene mutation sites (e.g., BRAF V600E), drug targets (e.g., PD-1), drug dosage, administration regimens, and pharmacokinetic (PK) and pharmacodynamic (PD) parameters. Units strictly follow international standards, such as mg for mass, nM for concentration, and days for time.

Constraints on Database and Operations

The characteristics of solid tumor R&D documents impose specific requirements on database and operations. First, diverse sources and complex formats require support for multiple file types, parsing, and storage, handling both unstructured and semi-structured data. Second, data update frequency is relatively low, but individual updates can be large. This demands efficient bulk import and version management capabilities to ensure data integrity and traceability. Third, documents contain extensive specialized terminology and cross-references, requiring high semantic understanding and entity recognition. This necessitates a high-performance vector database for precise recall. Additionally, data involves highly sensitive research findings and patient privacy, requiring stringent data security, access control, and audit logging. For high-concurrency queries, especially when multiple R&D teams simultaneously conduct literature reviews or data analysis, database read/write performance and stability are critical. The system must effectively handle concurrent requests, preventing service delays or overload-induced error responses like 429 Too Many Requests.

Configuration Settings

Configuration Item	Suggested Value	Rationale for Value
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Solid tumor research reports often contain large images and high-resolution data, leading to large file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large PDF documents or complex tables can be time-consuming; this provides sufficient processing time.
`Chunk size` (Segment Length)	`800–1200 characters`	Ensures completeness of specialized terms and context in the solid tumor domain, avoiding critical information truncation.
`Recall count` (Recall Count)	`Top 10 entries` (Top 10)	Increases initial recall coverage, providing richer context for re-ranking.
`Similarity threshold` (Similarity Threshold)	`0.75`	Balances precision and recall, reducing irrelevant results while not missing critical literature.
`maxContext`	`32000 token`	Solid tumor R&D reports have tightly linked contexts; this provides a sufficiently long context window.

Common Pitfalls

Critical entity fields in query results are empty or incomplete: This often occurs when the document parser fails to correctly identify entity fields in specific table formats or nested structures, leading to data extraction failures.
Frequent SQL query returned no data errors in the database connection workflow: This might be due to field names referenced in SQL statements not matching the actual database table structure, or query conditions improperly handling data types specific to solid tumors (e.g., gene sequence strings).
System returns 429 Too Many Requests errors under high concurrent requests: This indicates that the backend API or external model service lacks appropriate concurrency limits or request queues, leading to instantaneous request volume exceeding processing capacity.

Verification Steps

Randomly select solid tumor R&D documents in different formats (PDF, Word, Excel). After upload, check if parsed critical fields (e.g., TNM staging, gene mutation site, drug target) are complete and accurate.
Execute a series of complex queries containing solid tumor-specific terminology. Check the relevance of recall results and manually evaluate if the Similarity threshold (similarity threshold) is appropriate.
Simulate high concurrency scenarios. Observe system response times and error logs to confirm no 429 status codes or significant delays occur under expected concurrency. Evaluate the effectiveness of database connection pool and external service concurrency configurations.
Check the version management functionality for stored documents in the database. Ensure each document update generates a new version record and allows tracing back to historical versions.

Note: The values provided are common starting points. Measure against your own samples for optimal configuration.

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.