Data Characteristics
Data for solid tumor product and reagent consulting comes from clinical trial reports, drug inserts, medical journal literature, bioinformatics databases (e.g., TCGA, COSMIC), and manufacturer technical documentation. Update frequencies vary; clinical trial data is typically released periodically after research progress or approval, while bioinformatics databases update quarterly or semi-annually. Document structures often include extensive unstructured text, such as pathology descriptions, treatment plans, mechanisms of action, indications, contraindications, and adverse reactions. Structured data like drug molecular formulas, target information, dosage units (e.g., mg/kg), and experimental results are also present. Common fields include CAS number, target name, IC50, KD value, and gene mutation site. Units include concentration units like nM, μM, and time units like days, weeks, months.
Constraints on Deployment and Upgrades
The diverse and unstructured nature of solid tumor data demands high document parsing capabilities from a RAG (Retrieval-Augmented Generation) system, especially when processing PDF clinical reports and scanned documents. Irregular data updates require the deployed model to support flexible incremental update mechanisms to ensure the timeliness and accuracy of consulting content. The abundance of specialized terminology and biomedical entities requires FastGPT to effectively identify and associate these entities during vectorization and retrieval, preventing inaccurate recall due to semantic understanding deviations. Additionally, sensitive clinical data may have access restrictions, necessitating consideration of data source compliance during deployment. For numerical fields like IC50 and KD value, precise numerical matching and range retrieval are critical, as traditional text matching may be insufficient.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Clinical trial reports and large drug inserts can contain numerous images and charts, resulting in large file sizes. |
Chunk size (Segment Length) | 800 characters (characters) | Solid tumor document paragraphs are often long, containing complex medical background and argumentation; overly short segments can lose context. |
Recall count (Recall Count) | 8 entries (items) | Increases retrieval recall, covering more potentially relevant medical details and experimental data. |
Similarity threshold (Similarity Threshold) | 0.75 | Balances recall precision with generalization ability, ensuring retrieved results are highly relevant to solid tumor consulting. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Accommodates the parsing time for large PDFs and complex documents, preventing file processing failures due to timeouts. |
maxContext | 3000 Tokens | Ensures the model can process a sufficiently long context to understand multi-faceted information about solid tumor products. |
Common Pitfalls
- Local testing functions correctly, but after deployment to a Docker container, the database connection fails. This typically results from network configuration discrepancies between the Docker container and the host environment, or incorrect mapping of database addresses or ports.
- FastGPT exhibits insufficient retrieval results or poor relevance when processing solid tumor documents with extensive specialized terminology. This likely stems from a segmentation strategy unsuitable for long medical texts, leading to truncation of critical information or semantic loss.
- Queries or search terms that worked in older versions only show a single result in updated versions. This may relate to optimizations in document parsing and keyword extraction logic after a version upgrade, causing a change in display strategy.
Verification
- Upload and parse a typical solid tumor product insert PDF. Check if the file parsing status is successful and if the parsed text content is complete and free of garbled characters.
- For multiple solid tumor products, ask questions about their mechanism of action, adverse reactions, and indications. Verify that FastGPT's answers are accurate and detailed, comparing them against the original document information.
- Simulate an incremental data update by uploading a new supplementary clinical trial report. Subsequently, query for key new information from that report to confirm the system can timely incorporate and retrieve the latest data.
Note: 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.