Preclinical Safety Assessment Pharmacovigilance HTTP

Data Characteristics

Preclinical safety assessment data originates from pharmacology and toxicology research reports, GLP (Good Laboratory Practice) experimental records, safety assessment databases, and relevant literature. This data typically exists as structured tables, unstructured text (e.g., pathology reports, animal observation records), and images (e.g., tissue sections, electrocardiograms). Data update frequency is relatively low, primarily concentrated at different milestone stages of new drug development. Document structure is rigorous, adhering to ICH (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use) guidelines or NMPA (National Medical Products Administration) regulations. Core fields include test article information, animal strain, dose, administration route, observation indicators (e.g., body weight, organ coefficients, blood biochemical indicators, urinalysis, histopathological descriptions), and adverse event (AE) descriptions and severity. Units are strictly standardized; for example, dose units are typically mg/kg, and blood indicators include specific measurement values and their units.

Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"

The rigor and compliance requirements of preclinical safety assessment data demand high stability and data accuracy from HTTP interfaces. Due to the low data update frequency, focus is required on the completeness and version management of historical data to prevent assessment deviations caused by data overwrites or omissions. The complex document structure necessitates flexible data parsing capabilities in HTTP interfaces to handle mixed structured and unstructured data. For instance, extracting pathological descriptions from PDF reports or obtaining complete blood count data from LIMS (Laboratory Information Management System). Field and unit standardization means strict type validation and unit conversion are mandatory during interface transmission to ensure data consistency. When dealing with large volumes of animal experiment data, interfaces must support batch transmission and efficient processing, while also requiring high data transmission security (e.g., encrypted transmission) to protect the confidentiality of research data.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`requestTimeoutSeconds`	`600 seconds`	Preclinical safety assessment reports often contain large amounts of data, requiring longer processing times; this prevents request timeouts.
`maxPayloadSizeMB`	`100 MB`	Supports uploading large PDF reports or JSON data packages containing multiple images.
`dataSchemaValidation`	`Enable strict validation`	Ensures incoming data's field types, units, and formats comply with predefined specifications, preventing data corruption.
`authenticationMethod`	`OAuth 2.0`	Provides secure external system access credential management, aligning with pharmaceutical industry data security standards.
`errorHandlingStrategy`	`Retry 3 times, 5-second interval`	Addresses transient network fluctuations or occasional external system failures, improving data transmission success rates.
`metadataExtractionRules`	`JSONPath expression`	Precisely extracts key metadata from reports, such as study ID, test article name, and experimental date.

Common Pitfalls

Symptom: API call returns messages is empty error. Cause: The data structure in the request body does not match the messages field expected by FastGPT, or critical input fields are empty.
Symptom: After external system data synchronization, some field content is missing or displayed as garbled characters in FastGPT. Cause: The HTTP interface did not correctly specify the encoding format when transmitting non-ASCII characters, for example, not setting it to UTF-8, leading to data parsing failure.
Symptom: Numerous 400 Bad Request errors appear in logs, but the request content seems correct. Cause: The data passed by the external system has subtle inconsistencies in units or data types, such as a string passed to a numeric field, or mismatched dose units, failing FastGPT's strict data validation rules.

Verification Steps

Use FastGPT's API debugging tool to simulate a call with a typical preclinical safety assessment report data sample. Check if data is successfully received and parsed.
Within the FastGPT platform, review the imported preclinical safety assessment data in the knowledge base. Verify that key fields (e.g., test article name, dose, main observation results) are completely consistent with the original data source and free of garbled characters.
Perform content-based retrieval tests on several imported reports. Check if relevant document snippets are accurately recalled and evaluate the relevance threshold of the recall results.
Check FastGPT's system logs to confirm no connection timeouts, data validation failures, or authentication failures occurred during the HTTP interface calls.

Note: The values provided are common starting points. Measure them against your own samples for optimal performance.

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.