Data Characteristics for this Category
Solid tumor registration documents primarily include clinical trial reports, non-clinical study reports, manufacturing process and quality control files, and pharmaceutical research data. Data sources typically include Electronic Medical Record (EMR) systems, Clinical Trial Management Systems (CTMS), Laboratory Information Management Systems (LIMS) from medical institutions, and internal enterprise document management systems. The update frequency for these documents is relatively low, concentrating on different stages of clinical trials and critical points in the submission cycle. Document structures are highly standardized, adhering to ICH guidelines and NMPA technical requirements, such as the Common Technical Document (CTD) format. Fields and units are highly specialized. For example, clinical trial data involves patient baseline characteristics, dosage, adverse events (AE), and efficacy indicators (e.g., Objective Response Rate (ORR), Progression-Free Survival (PFS)). Units include milligrams (mg), milliliters (mL), days, and percentages (%). Pharmaceutical research data involves indicators like purity, content, and stability, with units such as percentages and ppm.
Constraints on "Deployment and Upgrade" Imposed by These Characteristics
The standardized and specialized nature of solid tumor registration documents requires FastGPT instances to have robust structured and unstructured data processing capabilities. Low update frequency means a large initial data import volume but fewer subsequent incremental updates. This demands high stability and efficiency from data migration tools. The CTD document structure challenges Retrieval Augmented Generation (RAG) recall strategies, requiring precise targeting of specific sections or paragraphs. The presence of specialized fields and units necessitates that the model correctly identifies and applies them during comprehension and generation, preventing information discrepancies due to unit confusion or field misinterpretation. During upgrades, due to the sensitive and critical nature of the data, any downtime or data inconsistency issues can have severe consequences. Therefore, upgrade processes must ensure high availability and rollback capabilities.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Individual clinical trial or pharmaceutical files can be large |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large PDF documents can take a long time to parse |
Chunk size (Chunk Length) | 800–1200 characters | Accommodates average paragraph length in CTD documents, maintaining semantic integrity |
Recall count (Recall Count) | Top 8 entries | Ensures coverage of highly relevant key information |
Similarity threshold (Similarity Threshold) | 0.75 | Improves recall precision, reduces irrelevant information interference |
Rerank result count (Reranked Return Count) | Top 5 entries | Further optimizes ranking, enhances final result quality |
Three Common Pitfalls
- Symptom: After a FastGPT version upgrade, specific fields (e.g., adverse event grades) appear confused or missing in generated responses. Reason: The new model or tokenizer version changed its understanding of specific professional terms, and sufficient regression testing was not performed.
- Symptom: After private deployment, the AI platform does not display the thinking process or directly outputs
<think></think>tags. Reason: TheOUTPUT_THINKING_PROCESSparameter was not correctly enabled in the deployment configuration, or a specific model version adjusted the output format for the thinking process. - Symptom: When importing large clinical trial report PDFs, the system times out or fails to parse the file. Reason:
PARSE_FILE_TIMEOUT_SECONDSis set too low, or the file's complex content caused the parsing engine to exceed the expected processing time.
How to Verify Correct Configuration
- Upload a clinical trial report containing complex tables and specialized terminology. Check if the chunking is reasonable, field recognition is accurate, and then attempt to ask questions, verifying the correctness of key data and units in the answers.
- In the FastGPT management interface, check if critical parameters like
OUTPUT_THINKING_PROCESSare enabled as expected. Execute a question-and-answer session and observe if the output includes a complete chain of thought. - Perform a complete version upgrade process. Before and after the upgrade, compare the stability of core functions and data consistency. Ensure that the recall and generation results for all preset sensitive data fields (e.g., PFS, ORR) are unbiased.
- Select a representative solid tumor registration document. Simulate questions to verify if FastGPT's recall count and similarity threshold accurately locate relevant sections and data. Assess if the recalled content is comprehensive and free of redundancy.
The values provided are common starting points and should be measured against specific 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.