Deployment and Upgrade for Phase II-III Clinical Products

Data Characteristics for This Category

Phase II-III clinical trial data originates primarily from Clinical Trial Management Systems (CTMS), Electronic Data Capture (EDC) systems, Laboratory Information Management Systems (LIMS), and external laboratory reports. Data update frequencies vary; some are real-time, while others are batch-imported. Data typically exists as CSV, XML, PDF reports, or structured database records. Document structures are complex, including protocols, Case Report Forms (CRFs), informed consent forms, adverse event reports, and laboratory test results. Field names are highly specialized, involving medical terminology, dosage units (e.g., mg/kg, IU), time points (e.g., D1, W4), and measurement units (e.g., mmol/L, ng/mL). Data volumes are large and often include unstructured text descriptions.

Constraints Imposed by These Characteristics on "Deployment and Upgrade"

The complexity and specialized nature of Phase II-III clinical data impose specific requirements on FastGPT deployment and upgrades. Data source diversity necessitates configuring multiple data connectors and ensuring stable data extraction and synchronization. Varying update frequencies require a flexible incremental update mechanism to avoid full re-indexing, thereby reducing resource consumption. Complex document structures, especially large volumes of unstructured text, demand robust text processing capabilities, such as custom tokenizers for accurate medical terminology recognition. Specialized fields and units require preprocessing before vectorization, such as unit standardization or numerical normalization, to improve retrieval accuracy. Large data volumes directly impact vector database storage capacity planning, computing resource allocation, and the time window for data migration and index rebuilding during upgrades.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`1000 MB`	Clinical report files (e.g., PDFs) can be large; this ensures complete uploads.
`Chunk size` (Segment Length)	`800–1200 characters` (characters)	Balances contextual coherence with information density per segment, suitable for lengthy clinical texts.
`Overlap Length`	`150 characters` (characters)	Ensures information at segment boundaries is not lost, especially for medical terms and related concepts.
`maxContext`	`16384 token`	Accommodates longer clinical Q&A contexts, including multi-turn interactions and detailed medical record information.
`Recall count` (Recall Count)	`Top 10 entries` (top 10)	Increases the probability of recalling relevant segments from a large knowledge base, improving coverage.
`Similarity threshold` (Similarity Threshold)	Calibrate based on actual measurements	Adjust according to the precise matching requirements of clinical terminology and data characteristics, preventing semantic drift.

Three Common Mistakes

After an upgrade, a simple application model configuration fails, and a non-preset model is called during chat. This occurs because the storage structure or default values of some configuration items change after a version upgrade, leading to old configurations not being correctly mapped to the new version.
After Docker deployment, entering the workflow shows a connection error. This is due to inconsistencies between container network configuration and host port mapping, or the database connection string not being correctly updated after the upgrade.
After system deployment, login fails due to incorrect username or password. This happens when authentication information in environment variables or configuration files (.env file) is not correctly loaded or is overwritten during an upgrade or migration.

How to Verify Correct Configuration

Check the admin backend under System Settings -> Model Management to ensure all expected base models and custom models are loaded and callable.
Randomly select 5-10 clinical documents of different types (e.g., CRF, lab reports), upload them to the knowledge base, and observe the segmentation results. Verify that critical information (e.g., drug name, dosage, adverse event type) is not incorrectly split or lost.
Conduct simulated Q&A tests. Ask complex questions related to different clinical trial phases (e.g., drug dosage adjustment, adverse event management). Evaluate the accuracy, completeness, and whether the cited knowledge points originate from the correct clinical data sources. Also, check the performance of Recall count (Recall Count) and Similarity threshold (Similarity Threshold) in actual Q&A.

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.