Molecular Diagnostics Pharmacovigilance HTTP Interface and

Data Characteristics in this Category

Molecular diagnostics pharmacovigilance data originates from clinical trial reports, real-world evidence (RWE), genomic sequencing results, biomarker detection reports, and electronic health records (EHR). Data update frequency varies by source. Clinical trial data typically updates when interim or final reports are released. RWE data may update quarterly or annually. EHR data generates in real-time or near real-time. Document structures are complex. They often include unstructured text descriptions, semi-structured tabular data, and structured gene locus information. Fields are highly specific. Examples include gene mutation types (e.g., SNP, Indel), allele frequency (Allele Frequency), biomarker concentrations (e.g., ng/mL, IU/L), and adverse event codes (e.g., MedDRA terms). The data volume is large and heterogeneous, requiring integrated processing.

Constraints from these Characteristics on the "HTTP Interface and External Systems" Component

Diverse molecular diagnostics data sources require highly flexible HTTP interfaces. These interfaces must adapt to different source system data protocols and authentication mechanisms. High-frequency data updates, especially from EHR or real-time monitoring devices, demand real-time interface performance, concurrent processing capabilities, and error retry mechanisms. The coexistence of unstructured and semi-structured data means interfaces must support multiple data formats upon reception, such as JSON, XML, and text files. This also requires robust parsing capabilities on the backend. Specific fields, like gene loci and drug targets, mean interface design needs to reserve sufficient data structure extensibility. It must also ensure accurate identification and mapping of these critical fields. Large data volumes require interfaces to support batch uploads and resume broken transfers. They also need reasonable limits on parameters like Content-Length.

Configuration Settings

Configuration Item	Suggested Value	Rationale for this Value
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Accommodates document uploads containing raw gene sequencing data or large clinical reports
`maxContext`	`2000 characters`	Balances the detail of molecular diagnostic reports with vector database indexing efficiency
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Allows for thorough parsing of large, complex molecular diagnostic reports
`Chunk size`	`800–1200 characters`	Ensures individual text segments contain sufficient context to support the completeness of gene mutation and adverse event information
`Recall count`	`Top 10 entries`	Improves the accuracy of retrieving relevant molecular diagnostic information from complex knowledge bases
`Similarity threshold`	`0.75`	Ensures high relevance of retrieval results to molecular diagnostic queries, filtering out irrelevant information

Three Common Mistakes

External systems call the FastGPT interface. The event field in the payload is not set correctly. This leads to incomplete event logging or triggers unexpected processing workflows.
Uploading a large number of molecular diagnostic md documents. The file size or quantity exceeds default interface limits. This causes some documents to fail knowledge base import. The system returns a 413 Request Entity Too Large error.
Processing molecular diagnostic reports from different laboratories. Inconsistent field naming or unit mismatches occur. This leads to data parsing failures or information loss. System logs show KeyError or ValueError.

How to Verify Correct Configuration

Use FastGPT's API interface. Simulate uploading a typical molecular diagnostic report containing gene sequencing data and adverse event descriptions. Observe if a 200 OK status code returns successfully.
In the knowledge base, search for keywords related to the uploaded report. Compare retrieval results with the original report content. Ensure critical information (e.g., gene mutations, drug names, adverse reaction descriptions) is accurately extracted and indexed.
Check FastGPT's backend log system. Confirm no ERROR level logs appear during data parsing, especially regarding data format, field matching, or timeout errors.
Test multiple external systems simultaneously pushing data to the FastGPT interface under high concurrency. Monitor interface response times and error rates. Ensure system stability under heavy load.

Note: The values provided are common starting points. Measure them 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.