HTTP Interface and External Systems for Stability Study

Data Structure for this Category

Stability study data originates from Laboratory Information Management Systems (LIMS), environmental monitoring systems, and manual records. Updates typically begin after batch production and include data entry at predefined time points (e.g., 3, 6, 12, 24, 36, 48, 60 months). Document structures are often structured or semi-structured reports. They include fields such as batch number, sample number, test item, test method, test result, unit, test date, storage conditions, and expiration date. Data units vary, for example, temperature ℃, humidity %RH, content mg/mL or %, and degradation products ppm. Some fields may contain complex text descriptions or chart links.

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

The multi-source nature of stability study data requires HTTP interfaces to handle various data formats from different systems. This may necessitate data cleansing and standardization. The periodic update pattern means interfaces must support incremental synchronization or scheduled full data pulls, avoiding reprocessing historical data. The semi-structured nature of documents demands parsing capabilities. The system needs to identify and extract key fields, especially numerical data with units or special characters. For example, the test result field may require semantic understanding in conjunction with the unit field. Additionally, large volumes of historical batch data and test point data can lead to large single request data sizes, directly impacting interface performance and timeout settings. Chart links may require additional HTTP requests to retrieve image content or metadata.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`HTTP_TIMEOUT_SECONDS`	`120 seconds`	Handles potentially large volumes of historical test data and multiple documents within a single request, preventing connection timeouts due to excessive data.
`MAX_FILE_SIZE_MB`	`200 MB`	Accounts for individual stability reports that may contain high-resolution images or embedded charts, providing ample file upload space.
`CHUNK_SIZE_CHARS`	`800–1200 characters`	Stability reports contain extensive descriptive text and test data. This range balances semantic completeness and recall efficiency.
`METADATA_FIELDS`	`Batch Number, Sample Number, Test Item, Test Date`	These fields are core identifiers for stability studies, facilitating precise retrieval and filtering.
`DATA_UPDATE_CRON`	`0 0 3 * * ?`	Executes data synchronization daily at 3 AM. This avoids peak business hours and promptly retrieves the previous day's latest test results.
`ERROR_RETRY_COUNT`	`3 times`	Given the instability of external systems, multiple retries increase the success rate of data synchronization.

Three Common Pitfalls

Symptom: After importing stability study reports, some numerical fields (e.g., content, degradation products) are empty or parsed incorrectly. Reason: The HTTP interface fails to correctly identify or convert numerical values with units during parsing, such as 99.5% or 15 ppm. It may also ignore variations in unit representation across different reports.
Symptom: After synchronizing data via the HTTP interface, documents found in the knowledge base have low relevance and do not accurately match user queries. Reason: The external system's interface document structure is complex. Key metadata such as Test Item and Storage Conditions were not sufficiently extracted during import, leading to a lack of effective indexing in the knowledge base.
Symptom: Importing Java interface documents fails after changing the file extension to .txt. Alternatively, when importing CSV files, the knowledge base content does not match the original data. Reason: The system's default file parser cannot recognize the modified file type or specific CSV structure. This prevents correct word segmentation and indexing of content. For example, the Batch Number field in a CSV file may be incorrectly recognized as a single long text string.

Verification Steps

Upload a typical stability study report (including numerical values, units, chart links) through the FastGPT management interface. Check if the document's Status in the File List is Completed. Verify that the Metadata fields correctly extracted Batch Number and Test Item.
Use the API Debugging Tool to send a simulated external system HTTP request. Include a JSON or XML payload with multiple batch data entries. Observe if the returned Status Code is 200. Confirm that the logs do not show HTTP_TIMEOUT_SECONDS-related errors.
In the knowledge base, use query statements containing keywords like Batch Number, Test Item, and Test Result for the imported stability study data. Evaluate the Relevance Score of the recalled results and the accuracy of the returned documents.
Check system logs. Confirm that the DATA_UPDATE_CRON scheduled task triggers as planned. After each execution, ensure the logs do not show ERROR_RETRY_COUNT reaching its limit, indicating that the data synchronization process is normal.

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.