Data Characteristics in this Category
R&D documents in academic promotion typically originate from clinical trial reports, drug inserts, medical guidelines, and research papers. These documents have a relatively stable update frequency, with concentrated updates occurring when new drugs are launched or guidelines are revised. Document structure varies; some follow standardized clinical report templates, while others are free-form research reviews. Core fields include drug name, indications, dosage and administration, adverse reactions, mechanism of action, clinical data (e.g., P-value, OR, HR), subject characteristics, and follow-up duration. Units involve dosage (mg, g), concentration (μg/mL, nM), time (days, weeks, months), and statistical indicators (%); these are often accompanied by specific medical terminology and abbreviations.
Constraints on Deployment and Upgrade from these Characteristics
Document source diversity requires FastGPT to support flexible integration with various data sources during deployment, such as PDF, Word, and HTML. It also needs robust document parsing capabilities. The periodic update frequency necessitates configuring scheduled tasks or webhooks to trigger data synchronization and model retraining. The presence of semi-structured documents demands higher robustness and field extraction accuracy from the parser, possibly requiring customized parsing rules. The abundance of specialized terminology and statistical units means the model requires pre-training or fine-tuning with specific domain knowledge to ensure accurate semantic understanding. For numerical fields in clinical data, extraction must support subsequent numerical comparison and analysis, which influences the choice of data index storage format.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 200 MB | Clinical trial reports or medical guidelines are often large; large file uploads must be supported. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Complex PDF or Word document parsing can be time-consuming; avoid parsing timeouts. |
Chunk size | 800–1200 characters | Academic documents have strong contextual relevance; maintain paragraph integrity to reduce semantic fragmentation. |
Recall count | Top 10–15 entries | Ensure coverage of all relevant key information points in the document. |
Similarity threshold | Calibrate by actual measurement, 0.75–0.85 suggested | Balance recall and accuracy; avoid interference from irrelevant information. |
Rerank result count | Top 5 entries | Highlight the most relevant information; improve the conciseness of the final output. |
Common Pitfalls
- After restarting the FastGPT service, the workbench's password-free sharing links become invalid, preventing external team members from accessing. This usually occurs due to improper configuration of environment variables like
SHARE_URL_PREFIX, which are not correctly persisted or loaded after a container restart, leading to incomplete or incorrect sharing links. - Model configurations set via environment variables in the
docker-compose.ymlfile do not take effect, causing model behavior to deviate from expectations. This phenomenon may stem from specific FastGPT versions (e.g., after 4.8.20) migrating model configuration to page management, which reduces or eliminates the priority of external environment variables. - After document parsing, numerical values for specific medical statistical indicators (e.g., P-value, OR) are extracted as empty or in an incorrect format. This may be due to the default parser's insufficient ability to recognize complex tables or non-standard text formats, requiring customized preprocessing steps or regular expressions for precise extraction.
Verification Steps
- Upload a clinical trial report PDF containing complex tables and multiple pages. Verify that the document is fully parsed in the FastGPT workbench knowledge base and that key data points from the report can be retrieved via keyword search.
- Use a query containing various medical terms and abbreviations. Verify that the model's response accurately understands these terms and cites relevant passages from the document.
- Configure a scheduled synchronization task in FastGPT. Upload a new drug insert. Verify that the scheduled task triggers as expected and correctly indexes the new document content into the knowledge base.
The values given 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.