Deployment and Upgrades for Molecular Diagnostics Products

Molecular Diagnostics Data Characteristics

Molecular diagnostics product data primarily consists of experimental results from gene sequencing, PCR testing, and immunohistochemistry. Data sources include raw data files (e.g., FASTQ, BAM, VCF formats) generated by sequencers, PCR machines, and mass spectrometers, as well as unstructured text like experimental reports and clinical interpretation documents. Data update frequency typically aligns with experimental batches, occurring daily, weekly, or monthly. Document structures are complex, containing extensive specialized terminology, gene locus information, mutation types, pathological descriptions, and diagnostic recommendations. Fields and units are biologically specific, such as gene names, chromosome positions, base pair counts, sequencing depth, and Ct values, often accompanied by specific version numbers and reference genome information.

Constraints on Deployment and Upgrades from Data Characteristics

Molecular diagnostics data characteristics impose specific requirements on FastGPT deployment and upgrades. Raw data files are typically large, requiring ample storage and efficient file upload mechanisms. The specialized terminology and complex structures in unstructured text demand that FastGPT accurately identifies and understands them during chunking and vectorization. This directly influences the settings for Chunk Length and Recall Count. Data update frequency dictates the knowledge base synchronization and index rebuilding cycles, requiring FastGPT to have flexible scheduled tasks or API triggers. Additionally, sensitive information within the data (e.g., patient genetic data) necessitates high standards for system security, permission management, and data anonymization. During deployment, pay attention to security-related parameters like FastGPT_PASSWORD_SALT. When upgrading, new versions with optimizations for specific data formats or model algorithms may require reprocessing historical data to ensure knowledge base accuracy and timeliness.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Molecular diagnostics raw data files (e.g., FASTQ) are large and require support for big file uploads.
`Chunk Length`	`800–1200 characters`	Experimental reports and interpretation documents have long paragraphs containing multiple specialized pieces of information; this balances semantic completeness with recall accuracy.
`Recall Count`	`Top 10`	Ensures query results cover multiple relevant gene loci or pathological features, preventing critical information from being missed.
`Similarity Threshold`	`0.75`	Molecular diagnostics terminology demands high precision. A threshold too low may introduce irrelevant results; too high risks missing relevant information.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Processing complex PDF reports or large text files can take considerable time, preventing parsing timeouts.
`embeddingModel`	`text-embedding-ada-002` or domain-specific model	Captures semantic relationships of specialized terms in the biomedical field, improving vectorization quality.

Common Pitfalls

Encountering a 403 status code (no body) error during model testing may indicate incorrect backend API key or permission configuration, leading to model service access denial.
Query results not reflecting the latest content after knowledge base document updates typically occur because knowledge base re-indexing was not triggered or scheduled tasks did not execute as expected.
Query results containing a large amount of irrelevant or redundant information usually result from improper Chunk Length settings, leading to fragmented semantics or excessive redundant chunks.

Verification Steps

Upload a test report containing common gene mutations and clinical significance, then perform a query to check if the results accurately link to relevant knowledge points.
Verify the knowledge base update mechanism: modify content in an uploaded document and observe if the knowledge base automatically updates and reflects these changes within the set period.
Upload a large raw data file (e.g., a several hundred MB FASTQ file) via the API to confirm successful upload and parsing.
In the FastGPT interface, check the MongoDB connection status to confirm that the database service is running normally and can read and write data correctly.

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.