Data Characteristics in this Category
Laboratory service data for clinical trial pre-screening primarily originates from experimental platforms such as high-throughput sequencing, mass spectrometry, and flow cytometry. It also includes pathology reports and imaging data. This data typically exists in structured (e.g., clinical biochemical indicators, gene mutation sites) and semi-structured (e.g., raw sequencing data files, proteomics reports) formats. Update frequency depends on the experimental cycle and sample size, usually weekly or monthly in batch updates. Document structures are complex, containing fields like sample ID, test item, result value, unit, and reference range. For example, gene sequencing data might contain millions of base pair locations, while mass spectrometry data involves thousands of protein or metabolite abundances. Data field names may include industry abbreviations and platform-specific identifiers. Units are diverse, such as ng/mL, nM, copies, and reads, requiring standardized handling.
Constraints Imposed by these Characteristics on "Deployment and Upgrade"
The complexity of laboratory service data introduces specific requirements for FastGPT's deployment and upgrade. First, the large volume and diverse formats of data necessitate FastGPT's robust file parsing capabilities and efficient vectorization indexing, especially for pre-processing specialized file formats like FASTQ, VCF, and mzML. Second, the data update frequency dictates the periodic task scheduling for model training and knowledge base synchronization. Frequent data updates require configuring shorter knowledge base refresh intervals to ensure pre-screening results are based on the latest experimental data. Inconsistent field naming and diverse units demand strict cleaning and standardization during data import, which may involve custom data pre-processing scripts. Furthermore, sensitive biomedical data imposes high requirements for data security and permission management. The deployment environment must meet compliance standards and ensure encrypted data transmission. During upgrades, key considerations include new model compatibility, smooth migration of old knowledge bases, and adaptation to data format changes.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | Accommodates uploading large sequencing or mass spectrometry raw data files |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Ensures complex files like VCF or mzML have sufficient time for parsing |
maxContext | 8000 tokens | Covers longer contextual information in clinical reports and experimental results, preventing truncation |
Chunk size | 800–1200 characters | Balances semantic integrity and vector recall efficiency, adapting to biomedical text characteristics |
Recall count | Top 10 entries | Increases coverage of relevant information during pre-screening, reducing missed detections |
Similarity threshold | Calibrated by actual measurement, typically 0.75-0.85 | Ensures recalled experimental data is highly relevant to clinical features, avoiding irrelevant information interference |
Three Common Mistakes
- After uploading an attachment, the chat window does not summarize the attachment or answer questions. This often occurs because the model is not loaded correctly or the file parsing service is misconfigured, preventing effective vectorization and indexing of file content.
- No output occurs after calling a database connection in a workflow. This may be due to incorrect database connection parameters, insufficient permissions, or a mismatch between the database query statement and the actual data schema, preventing the retrieval of expected data.
- The pop-up window at the start of a new conversation cannot be removed, or interface elements need modification. This often happens when a basic community version is deployed, where the frontend code does not provide direct configuration options, requiring modification of frontend code files for customization.
How to Verify Correct Configuration
- Upload a typical laboratory report PDF file. Observe if it is successfully parsed and a summary is generated. Check if the summary accurately reflects the key points of the report.
- Create a workflow that queries for specific gene mutations or biomarkers. Verify if it can recall relevant experimental data and clinical guidelines from the knowledge base. Confirm that the number of recalled items matches expectations.
- In the chat interface, enter a pre-screening related question, such as "Response of patients with XX gene mutation to YY drug." Check if the model's answer references experimental data from the knowledge base and verify the correctness of data sources and values.
- Check system logs to confirm that the file parsing service (e.g.,
FILE_PARSER_SERVICEmodule) has no errors and that knowledge base synchronization tasks execute at the expected frequency.
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.