Data Characteristics for This Category
Biomedical data for solid tumor products and reagents primarily originates from clinical trial reports, scientific literature, drug inserts, diagnostic kit instructions, and public regulatory databases. This data updates frequently, especially clinical trial results and new drug approval information, typically quarterly or semi-annually. Document structures are mainly unstructured text (e.g., PDF reports, Word documents) and semi-structured data (e.g., CSV, JSON for compound information, gene expression profiles). Fields include drug targets, mechanisms of action, indications, adverse reactions, dosage, administration routes, storage conditions, lot numbers, and expiry dates. Units cover common milligrams (mg), milliliters (mL), moles (mol), degrees Celsius (°C), and specific biological activity units (e.g., IU, U/mL).
Constraints Imposed by These Characteristics on "Forms and Interactions"
The high update frequency of solid tumor data requires form designs to quickly adapt to changes in data sources. For example, product batch and expiry date information needs dynamic updates. The coexistence of unstructured and semi-structured data means form input must support a hybrid of text parsing and structured field entry. For instance, a user might paste a scientific literature description, and the system extracts key information from it. Diverse fields and units necessitate flexible input validation mechanisms in forms to ensure data format correctness and prevent errors due to unit confusion. Additionally, the wide variety of products and reagents requires form interaction logic to support multi-level classification filtering, such as searching by target, indication, or reagent type, to help engineers precisely locate needed information.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 3000 Tokens | Solid tumor product descriptions and clinical data are often long, requiring a sufficient context window for processing. |
UPLOAD_FILE_MAX_SIZE | 500 MB | Supports uploading large PDF clinical trial reports and genomic data files. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing complex PDF documents and structured data files requires a longer parsing time. |
Recall count (Recall Count) | Top 10 entries (Top 10) | Ensures broader coverage of potentially relevant product or reagent information in initial retrieval. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Descriptions of solid tumor targets and mechanisms of action often have high similarity, requiring a balance between recall and precision. |
Rerank result count (Reranked Return Count) | Top 3 entries (Top 3) | After reranking, focuses on displaying the 3 most relevant product information entries. |
Common Mistakes
- The "Cannot convert undefined or null to object" error during workflow execution typically occurs when the output of a preceding AI conversation node is empty and not correctly passed as input to the next node.
- Retrieval results fail to include expected product information. This may be due to a
Similarity threshold(Similarity Threshold) set too high, filtering out relevant but slightly less similar documents. - Forms submit with a long response time or timeout. This often happens if
PARSE_FILE_TIMEOUT_SECONDSis set too short, preventing sufficient processing of large uploaded files or complex text content.
How to Verify Correct Configuration
- Upload solid tumor product manuals or clinical reports of different sizes and formats (e.g., PDF, CSV). Confirm successful file parsing and correct extraction of key fields.
- Simulate user queries for common solid tumor products or reagents. Check if the system returns accurate and complete product information, including critical data like dosage and lot numbers.
- Test complex queries with multiple filtering conditions. Verify that the form interaction logic responds correctly and effectively filters by target, indication, and other criteria.
- Examine input and output logs for each AI conversation node in the workflow. Ensure smooth data flow without null values or type mismatches.
Note: The values provided are common starting points. Measure against your own samples for optimal performance.
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.