HTTP Interface and External Systems for Culture Media and

Data Characteristics for This Category

Regulation data for culture media and consumables originates from internal quality management system documents, supplier qualification files, and product specifications. This data has a relatively low update frequency, typically revised periodically (e.g., semi-annually or annually) due to regulatory updates, product iterations, or internal process optimizations. Document structures are primarily PDF, Word, or structured text, containing numerous tables, diagrams, and regulatory citations. Key fields include product name, batch number, production date, expiry date, storage conditions, usage instructions, quality standards, risk level, supplier information, SOP number, and version number. Units involved include temperature (℃), humidity (%RH), concentration (g/L, mol/L), volume (mL, L), and shelf life (months, years).

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

The low update frequency of regulation data means HTTP interfaces do not require overly frequent polling of external systems for data synchronization. Periodic batch synchronization can be adopted, reducing system load. Complex document structures, including unstructured text and numerous tables, necessitate that HTTP interfaces can parse various data formats, for example, by extracting key information via OCR or intelligent document parsing services. Fields and units are highly standardized but can have multiple representations. The interface requires flexible data mapping capabilities to unify heterogeneous data from external systems into a FastGPT-recognizable internal format, especially for date, time, numerical values, and unit conversions. Data originates from internal systems, demanding high security. The interface must support authentication and authorization mechanisms to ensure compliant data transmission.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`datasource_sync_interval`	`168 hours`	Culture media and consumables regulations typically update monthly or quarterly, reducing unnecessary synchronization overhead.
`http_request_timeout`	`60 seconds`	Parsing and transmitting large documents like PDFs and Words can be time-consuming; this allows sufficient time.
`api_key_header_name`	`X-API-Key`	Most internal systems use custom HTTP headers to pass API keys, following existing conventions.
`max_document_size_mb`	`20 MB`	Regulation documents may include charts and attachments; limiting individual document size prevents network congestion.
`json_field_mapping`	`{"product_name": "materialName", "sop_version": "version"}`	External system field names may not align with FastGPT's internal representation, requiring explicit mapping.

Three Common Pitfalls

HTTP requests return 401 Unauthorized or 403 Forbidden status codes because the external system API key or access token is misconfigured or expired.
Key numerical values or date information are missing from knowledge base retrieval results. This occurs when the raw data format returned by the external system does not match expectations, leading to parsing failures.
During context concatenation, historical conversation information is duplicated or lost. This can happen if session_id or conversation_id are not correctly passed in external system API parameters.

Verification Steps

After calling the HTTP interface, check FastGPT logs to confirm no HTTP 5xx or 4xx error codes are present and that the data synchronization task status shows as successful.
Randomly select several culture media and consumables regulation documents. Use FastGPT's knowledge base retrieval to verify that key fields (e.g., batch number, expiry date, storage conditions) are correctly extracted and displayed.
Simulate user questions about specific SOP processes or consumable usage guidelines. Check if the AI's answers accurately cite relevant regulation content and if the cited source document links are accessible.

Note: The values provided are common starting points. Measure them against your 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.