HTTP Interface and External Systems for Supplier Audit

Data Characteristics

Supplier audit data for clinical trial pre-screening primarily comes from audit reports, qualification documents, historical collaboration records, Standard Operating Procedure (SOP) documents, and compliance checklists. This data typically exists as unstructured documents (PDF, Word) and semi-structured tables (Excel, CSV). Update frequencies vary; qualification documents might update annually, while audit reports generate with each audit event. Document structures are diverse. For example, audit reports often include sections like executive summary, audit scope, findings, improvement recommendations. Qualification documents cover company registration information, quality management system certifications, and personnel qualifications. Fields and units involved include supplier name, audit date, auditor, finding description, risk level, corrective actions, completion date, and compliance score. Risk levels might use a low/medium/high or 1-5 scale, and compliance scores often appear as a percentage.

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

The highly unstructured and semi-structured nature of supplier audit data demands robust data ingestion capabilities from the HTTP interface. Extracting key information, such as audit findings and risk levels, from PDF and Word documents requires support for multiple file formats and content extraction. The uncertain update frequency means the interface design must accommodate both bulk imports and incremental updates. For example, when a new audit report is received, it should trigger a single document's parsing and knowledge base update. Diverse document structures require the system to have flexible metadata extraction and custom field mapping capabilities during data processing. This adapts to different report formats across various suppliers and audit types. For instance, accurately identifying risk point descriptions and rectification completion dates from different audit report templates. For numerical data like compliance scores, the interface must ensure correct data types during transmission to avoid data distortion from floating-point precision issues.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`50 MB`	Audit reports and qualification documents often contain images and extensive text. This size is sufficient for most cases.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Processing complex PDF documents or large Excel tables can take a long time. This ensures sufficient parsing time.
`maxContext`	`16000 tokens`	Ensures complete capture of critical findings and recommendations in audit reports, reducing information loss due to text truncation.
`Chunk size` (Segment Length)	`800–1200 characters`	Balances semantic completeness and retrieval efficiency. This avoids segments that are too long or too short, which could affect recall quality.
`Similarity threshold` (Similarity Threshold)	`0.75` or measured empirically	Filters out irrelevant audit clauses, improving the accuracy of retrieval results and reducing interference from unrelated information.
`Rerank result count` (Reranked Return Count)	`Top 5`	Reranks the most relevant results after initial retrieval, improving the efficiency with which users obtain key information.

Three Common Mistakes

Symptom: After importing an audit report, key fields like risk level or corrective actions are often empty. Reason: The HTTP interface fails to accurately identify specific information areas in different audit report formats when processing unstructured documents, leading to critical data extraction failures.
Symptom: After submitting new supplier qualification documents via API, the knowledge base does not reflect the latest information promptly. Reason: External systems calling the interface do not correctly trigger incremental updates or index rebuilding processes, causing data synchronization delays.
Symptom: When calling an external system interface for data synchronization, an HTTP 400 Bad Request error is returned, indicating invalid_metadata_field. Reason: The external system passes metadata using custom field names not pre-configured in the knowledge base or with an incompatible format, leading to data validation failure.

How to Verify Correct Configuration

Select various formats of audit reports and qualification documents. Upload them via the HTTP interface. Verify that the parsed text content is complete and free of garbled characters.
Upload an audit report with clear risk level or compliance score. Check that the corresponding metadata fields in the knowledge base are correctly populated and compare them with the original document.
Simulate an external system calling the interface to submit incremental update data containing the latest supplier information. Verify that the relevant entries in the knowledge base are updated as expected and check the update timestamp.

The values provided are common starting points. Measure them against your own samples to determine the most suitable configuration.

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.