HTTP Interface and External Systems for Pharmaceutical

Data Characteristics for This Category

Pharmaceutical e-commerce registration data primarily originates from pharmaceutical manufacturers, medical device manufacturers, and the e-commerce platform's operational data. This data has a relatively low update frequency, typically submitted during product launch or significant changes. Document structures are complex, including drug inserts, scanned registration certificates, approval documents, inspection reports, clinical trial data, and enterprise qualification certificates. Most documents are in unstructured or semi-structured formats like PDF, Word, and Excel. Field content is highly standardized, such as drug generic names, specifications, dosage forms, and registration certificate numbers. However, different batches or sources may have naming discrepancies or inconsistent units (e.g., mg, g, ml, IU), and often contain extensive medical terminology.

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

The low update frequency of pharmaceutical e-commerce registration documents means real-time data synchronization is not necessary; periodic bulk imports are more suitable. The diverse document formats require HTTP interfaces to support various file types for upload and parsing, and to handle unstructured text extraction. The standardized yet potentially varied nature of fields demands robust data cleaning and standardization capabilities from external systems. HTTP interfaces must carry metadata when receiving data to assist subsequent processing. The complexity of medical terminology may necessitate preprocessing or validation using specific dictionaries during data transfer to ensure semantic accuracy and prevent registration errors due to misinterpretations.

Configuration Recommendations

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Individual submission files (e.g., scanned approval documents) can be large.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large PDF documents can be time-consuming.
`maxContext`	`4000 characters`	Accommodates the average information density of a single submission document segment.
`Chunk size`	`800–1200 characters`	Balances semantic completeness with model processing efficiency.
`Similarity threshold`	`0.8`	Ensures high relevance between recall results and submission materials.
`HTTP_REQUEST_TIMEOUT`	`120 seconds`	Addresses potential delays when external systems process complex requests or large files.

Common Pitfalls

An HTTP request returning a 504 Gateway Timeout status code usually indicates that an external system failed to respond within FastGPT's default request timeout while processing large file uploads or complex parsing tasks.
Key fields (e.g., registration certificate number, approval number) are empty in imported registration documents. This often occurs when external systems fail to accurately identify or extract these specific format fields from unstructured documents.
After an API call to the workflow, new knowledge bases are not created or files are not imported. This may be due to insufficient API key permissions or missing required parameters (e.g., knowledge base name, file path) in the request body.

Verification Steps

Upload a typical registration document containing various formats (PDF, Word, Excel). Observe if it parses correctly and extracts key information. Verify the completeness of the extracted fields.
Upload a submission file exceeding the expected size via the API interface. Verify if the UPLOAD_FILE_MAX_SIZE configuration takes effect and if the returned error message is as expected.
Simulate external system response delays. Check if the HTTP request timeout mechanism between FastGPT and the external system functions according to the HTTP_REQUEST_TIMEOUT configuration.

The values provided are common starting points and should be measured against specific 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.