HTTP Interface and External Systems for Supplier Audit

Data Characteristics

Supplier audit data originates from various documents: quality management system files, production site records, inspection reports, change control records, and deviation handling reports. Suppliers provide these documents. Common formats include PDF, DOCX, and XLSX. Content covers production processes, quality control, equipment calibration, and personnel qualifications.

Data update frequency varies. New suppliers or major changes generate large data volumes. Routine updates involve batch records and annual reviews. Documents typically follow GxP guidelines. Fields include batch number, production date, expiration date, inspection results, and equipment serial number. Units involve temperature (℃), pressure (MPa), concentration (% or ppm), and time (hours, days).

Constraints Imposed by Data Characteristics on HTTP Interface and External Systems

Supplier audit documents are often unstructured. They contain complex fields and diverse units. The HTTP interface must handle large file uploads and extract content from formats like PDF.

Data updates are irregular. External system integration requires event-driven or on-demand triggering, avoiding inefficient polling. Documents adhere to GxP guidelines. Data transfer and storage must ensure integrity and security, for example, by using HTTPS.

Field and unit specificities, such as batch number regex matching and numerical range validation for inspection results, require precise handling. This applies to HTTP request parameter construction and response parsing. This ensures accurate mapping to the internal data model.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`AIPROXY_API_ENDPOINT`	Actual deployed AI service address	Points to an internal or external AI service for document parsing and information extraction.
`AIPROXY_API_TOKEN`	API key with minimum permissions	Secures external system calls, preventing unauthorized access.
`maxContext`	`8000`	Accommodates the typically long text volume of audit documents, ensuring complete context.
`Chunk size`	`1000 characters`	Balances semantic integrity and processing efficiency, avoiding excessively large or small segments.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Provides sufficient time to parse large PDFs or complex format documents.
`http.timeout`	`30000 ms`	Addresses potential external system response delays, preventing task failures due to timeouts.

Common Pitfalls

HTTP request does not execute as expected, directly triggering AI conversation: Workflow conditional logic is not strict. For example, pre-conditions are not configured correctly, skipping the HTTP request node.
HTTP request body parameter fails to generate dynamically: Parameter expressions reference non-existent variable names, or variable types do not match expectations. This causes request body construction to fail.
File parsing fails or content is incomplete after upload: PARSE_FILE_TIMEOUT_SECONDS is set too short. Large audit reports do not complete parsing within the allotted time. Alternatively, the file format is unsupported, preventing content extraction.

Verification Steps

Use the workflow debugging feature. Observe the HTTP request node's input and output parameters. Confirm the body field content meets expectations and the status code is 200 or 201.
Upload a typical supplier audit report (e.g., a PDF with charts and text). Check the knowledge base for the document's segment count and content completeness, ensuring no significant omissions.
Construct a query with key fields (e.g., batch number, production date). Verify the AI's response accurately references and extracts relevant information from the audit report. Also, check unit correctness.
Examine system logs. Confirm AIPROXY_API_ENDPOINT and AIPROXY_API_TOKEN call records. Ensure no authentication failures or connection timeouts.

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.