Data Characteristics in This Domain
R&D documents in hematologic oncology include clinical trial protocols, research reports, pathology analyses, gene sequencing data, and drug mechanism literature. These documents originate from various sources, such as internal pharmaceutical company databases, public medical journals, and clinical research institution reports. Data update frequency is relatively high, especially for clinical trial progress and gene mutation information, with significant updates potentially occurring weekly or monthly. Document structures are complex, often containing large amounts of unstructured text, tables, graphs, and biological sequence information. Fields and units are highly specialized, for example, "Copy Number Variation (CNV)," "Microsatellite Instability (MSI)," "Complete Remission Rate (CR)," and "Progression-Free Survival (PFS)." Units involve concentration (nM), dosage (mg/kg), and time (months, years).
Constraints Imposed by These Characteristics on Database and Operations
The complex structure and high update frequency of hematologic oncology R&D documents challenge database design. Unstructured text requires efficient vector storage and retrieval capabilities. Structured information in tables and graphs demands flexible field parsing and indexing mechanisms. High update frequency means the database must support incremental synchronization and version control to ensure real-time information and traceability. Specialized fields and units require the database to store and correctly parse these specific data types, preventing semantic deviations during information extraction and comparison. For example, gene sequencing data is large and diverse in format, requiring high storage capacity and I/O performance. Time-series data in clinical trial reports, such as PFS curves, require the database to support efficient time-point queries and aggregate analysis.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates large file uploads such as gene sequencing reports and extensive clinical trial documents. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Allows sufficient processing time for complex PDFs and multi-page scanned documents, which take longer to parse. |
maxContext | 16384 | Ensures comprehensive understanding of lengthy research reports and literature abstracts. |
Chunk size | 800–1200 characters | Balances semantic integrity with model processing efficiency, reducing truncation of key information. |
Recall count | 15–25 entries | Increases coverage of relevant document segments, improving accuracy for complex queries. |
Similarity threshold | Calibrated by actual measurement, 0.75–0.85 recommended | Distinguishes highly relevant specialized terms from general medical vocabulary, avoiding noise while ensuring recall. |
Three Common Pitfalls
- Symptom: During development environment deployment, the MongoDB database connection times out, and the
pnpm devcommand fails to start the service. Reason: Network configuration or firewall restrictions prevent the local development environment from accessing the specified MongoDB instance port. - Symptom: When FastGPT queries MongoDB using a model, the token consumption differs from the actual token consumption of the API call. Reason: FastGPT preprocesses input or post-processes query results, increasing token usage.
- Symptom: The database connection module does not directly support configuring Oracle database types, preventing integration with existing drug R&D data sources. Reason: The current system design primarily targets NoSQL databases; specific relational databases like Oracle require additional driver and adapter layer development.
How to Verify Configuration
- Upload a hematologic oncology clinical trial report containing complex tables and graphs. Verify that its text and structured information are correctly parsed and stored.
- Perform a search including specialized terms and acronyms (e.g., "AML," "BCR-ABL"). Confirm that the relevance and accuracy of the recalled results meet the expected threshold.
- Monitor background logs to check the success rate and duration of file parsing tasks. Ensure no failures occur due to timeouts or insufficient resources.
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.