HTTP Interface and External Systems for Molecular

Molecular Diagnostics Data Characteristics

Molecular diagnostics quality documents include reagent kit batch release records, instrument calibration reports, standard operating procedures (SOPs), risk assessment reports, and deviation handling records. These documents typically exist as PDFs, Word files, or structured text. Some data may reside in Laboratory Information Management Systems (LIMS) or Electronic Lab Notebooks (ELNs). Data sources are diverse, originating from internal lab generation, supplier provision, and regulatory bodies. Update frequencies vary significantly; SOPs might be revised annually, while batch release records generate in real-time with each batch. Document structure varies; SOPs usually have fixed sections, but deviation records often contain extensive free-text descriptions. Common fields include batch number, expiration date, instrument serial number, test item, quality control results, and key parameters like Ct value and melting curve temperature. Units include ng/μL, copies/mL, ℃, and kPa.

Constraints from "HTTP Interface and External Systems" for These Characteristics

The diverse sources and formats of molecular diagnostics quality documents require HTTP interfaces with robust file parsing capabilities. For example, retrieving structured data from LIMS or ELN systems demands precise adherence to their API specifications, ensuring accurate mapping of critical fields such as batch number and expiration date. Introducing unstructured documents (e.g., PDF SOPs) requires the interface to trigger efficient Optical Character Recognition (OCR) and content parsing processes after upload. This extracts text content and identifies document structure. Real-time requirements for some documents (e.g., batch release records) necessitate HTTP interfaces that support high-frequency data synchronization and incremental update mechanisms. This avoids performance burdens from full synchronization. Standardization of fields and units is crucial. The interface must ensure unit consistency during data transfer, preventing data errors caused by confusion between μL and mL. For external system data, the interface must also handle data model differences between systems, performing necessary conversions and validations.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`API_KEY`	Provided by external system	Used for authentication, ensuring data transfer security.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accommodates OCR and parsing time for large PDF or Word documents, preventing timeouts.
`UPLOAD_FILE_MAX_SIZE`	`100 MB`	Balances document size and network transfer efficiency, meeting upload needs for large SOPs or reports.
`maxContext`	`8000 tokens`	Adapts to context window requirements for long texts like molecular diagnostics SOPs, ensuring semantic completeness.
`Chunk size` (Segment Length)	`500 characters`	Optimizes recall efficiency and relevance for long documents, balancing comprehension and retrieval granularity.
`Similarity threshold` (Similarity Threshold)	`0.75`	Ensures recalled quality documents are highly relevant to the query, reducing interference from irrelevant information.

Common Pitfalls

Symptom: After external system data synchronization, some critical fields like batch number or test result are empty. Reason: The HTTP interface's data mapping configuration is incorrect, failing to properly link external system fields with internal FastGPT fields. Alternatively, the external system API's data structure changed, but the interface was not updated.
Symptom: Uploading many PDF SOPs leads to prolonged system unresponsiveness or a 504 Gateway Timeout error. Reason: The PARSE_FILE_TIMEOUT_SECONDS configuration is too short, not providing enough processing time for complex document parsing (OCR, structured extraction).
Symptom: A new model API is configured, but the FastGPT interface displays "Invalid Token." Reason: The API_KEY is incorrectly configured or not saved properly, failing authentication with the target AI model, leading to rejected requests.

Verification Steps

Check FastGPT's backend logs for 200 OK or other successful status codes during HTTP interface interaction with external systems.
Upload a typical molecular diagnostics SOP or batch release record. Verify that the document's content is fully and correctly parsed in the FastGPT knowledge base, and that key fields like batch number and expiration date are recognized.
Use a query related to quality document content. Verify that FastGPT accurately recalls relevant document segments. Adjust the similarity threshold based on actual needs.

Note: The values provided are common starting points and should be measured against your 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.