HTTP Interface and External Systems for Culture Media and

Data Characteristics for This Category

Quality document data for culture media and consumables typically originates from supplier-provided COAs (Certificates of Analysis) or CoAs (Certificates of Origin), as well as internal inbound inspection reports. Document updates depend on batch arrivals and inspection cycles, usually occurring weekly or monthly per batch. Documents are primarily in PDF or scanned image formats, containing structured tabular data and unstructured text descriptions. Key fields include Batch_No, Prod_Date, Exp_Date, various physical and chemical indicators (e.g., pH, osmolarity, endotoxin content) and their units (e.g., mg/L, mOsm/kg, EU/mL), Test_Method, and Acceptance_Criteria.

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

The diverse data sources (PDFs, scanned images) for culture media and consumables quality documents require HTTP interfaces to support file uploads or provide file download links for subsequent OCR and content extraction. The medium-to-low update frequency implies that real-time requirements for external systems are not high, but periodic or batch-triggered data synchronization is necessary. The mix of structured tabular data and unstructured text within documents dictates that HTTP request bodies must flexibly pass structured parameters and receive responses containing both text and parsed table results. Standardization of field names (e.g., Batch_No, pH value) and units (e.g., mg/L) is crucial. This requires external systems to consider field mapping and unit conversion compatibility in API design, preventing issues caused by character encoding or data type mismatches, such as MySQL Chinese garbled characters.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`requestTimeout`	`60000 ms`	Accommodates response delays for large file uploads or complex parsing.
`maxChunkSize`	`5000 characters`	Balances semantic completeness of text with RAG retrieval efficiency.
`similarityThreshold`	`0.75`	Ensures high matching accuracy between query results and quality standards.
`headers.Content-Type`	`application/json` or `multipart/form-data`	Adapts to different interface types, such as structured data submission or file upload.
`body.batch_id`	Dynamically retrieve batch number	Enables precise document querying or updating based on batch number.
`retryAttempts`	`3 times`	Addresses network fluctuations or temporary external system failures.

Three Common Mistakes

HTTP module call fails, but Postman works. This usually indicates FastGPT environment network configuration issues (e.g., proxy settings), SSL certificate problems, or inconsistent User-Agent headers causing external interfaces to reject connections.
Database plugin fails to display Chinese characters. This often results from not specifying character set encoding (e.g., charset=utf8mb4) in the database connection string, or a mismatch in encoding between the database, tables, fields, and the FastGPT environment.
Multimodal model call reports parameter errors. This occurs when the format of images or other non-text data in the HTTP request body does not conform to API documentation requirements, or the Content-Type header is not correctly set to image/jpeg etc.

How to Verify Configuration

Execute an HTTP request involving a batch number query. Check if the returned results include the corresponding culture media and consumables quality indicators, and verify that key field values (e.g., pH value, endotoxin content) and units are correct.
Upload a PDF quality document containing complex tables. Trigger parsing via the HTTP interface, then query the document content to confirm that table data is correctly extracted and indexed.
Simulate a temporary unavailability of the external system. Observe if FastGPT's HTTP module retries according to the retryAttempts configuration and ultimately returns the expected failure or success status.
Examine FastGPT's log output for any encoding-related warnings or error messages, especially when processing documents containing Chinese characters or performing database queries.

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.