Data Characteristics in this Category
Molecular diagnostics data for clinical trial pre-screening primarily originates from laboratory reports such as gene sequencing, PCR testing, and immunohistochemistry. This data typically exists as structured or semi-structured text, including VCF (Variant Call Format) files, PDF or text outputs of sequencing reports, and CSV/JSON files exported from LIS (Laboratory Information System) systems. Data update frequency varies by test type and trial stage, ranging from daily updates (e.g., rapid PCR results) to weeks or months (e.g., whole-genome sequencing). Document structures are complex, often containing patient basic information, sample information, detection methods, gene loci, variant types, and clinical significance interpretations. Field names can be heterogeneous, and units vary, such as gene locus coordinates, mutation frequency percentages, or copy numbers.
Constraints Imposed by these Characteristics on Tool Calling and Plugins
The heterogeneity and complexity of molecular diagnostics data impose several constraints on tool calling and plugins. First, diverse data source formats require plugins with robust parsing capabilities, such as handling VCF, PDF, or custom JSON structures. Second, uncertain data update frequencies necessitate tool calling mechanisms that support flexible scheduling strategies, accommodating both real-time requirements and periodic batch processing. Furthermore, non-standardized data fields and inconsistent units make direct data comparison and filtering difficult, requiring plugins to perform data cleaning, standardization, and normalization. For example, the same gene variant locus might appear with different naming conventions across reports, or mutation frequencies might be expressed as decimals or percentages, increasing the difficulty of precise matching and requiring pre-processing or conversion within the tool calling logic.
Configuration Settings
| Configuration Item | Suggested Value | Rationale for this Value |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large-scale gene sequencing report file parsing can be time-consuming; this avoids parsing interruptions. |
maxContext | 800–1200 characters | Key variant information and clinical interpretation text in molecular diagnostic reports are lengthy, requiring sufficient context for semantic understanding. |
similarity_threshold | 0.75 | Matching gene variant descriptions and clinical phenotypes requires high similarity to ensure accuracy and reduce false positives. |
max_tokens | 2048 | For complex queries or multi-gene locus comparisons, model responses may include detailed explanations, ensuring complete output. |
plugin_retry_count | 3 | External detection systems or databases occasionally experience transient failures; increasing retries improves stability. |
extract_pattern | Calibrate based on actual measurement | Write regular expressions or JSONPath expressions for data extraction, targeting specific report formats (e.g., VCF or custom JSON). |
Three Common Pitfalls
- After calling an external report parsing tool, a specific field in the returned result is empty. The reason is that the external tool's API return format does not match the expected parsing path, leading to data extraction failure.
- An environment variable is called in the workflow for database connection, but during actual runtime, the model returns "message: Unable to connect to database." The reason is that the environment variable set during Docker deployment was not correctly passed to the FastGPT workflow runtime environment.
- When executing a plugin involving complex data processing, a "message: plugin execution timeout" error occurs. The reason is that the plugin's internal processing logic (e.g., gene sequence alignment) takes too long, exceeding the default execution time limit.
How to Confirm Correct Configuration
- Through FastGPT's debugging interface, observe whether the input parameters for tool calls are as expected, ensuring no data loss or format errors during transmission.
- In actual runtime scenarios, check the output results of the tool calling plugin and compare them with the original molecular diagnostic report to confirm that key fields (e.g., gene locus, variant type, clinical significance) are accurately extracted and standardized.
- Simulate various abnormal situations (e.g., non-standard report formats, network latency) to verify that error handling mechanisms are triggered as expected, such as correct retries or explicit error messages.
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.