HTTP Interface and External Systems for DTP Pharmacy

Data Characteristics

Data for DTP pharmacy registration preparation originates from pharmaceutical companies. This includes clinical trial reports, pharmaceutical research data, manufacturing process documents, quality standards, and stability study data. Regulatory documents and guidelines from drug administration authorities also contribute. Data updates are infrequent, typically occurring with drug development progress or policy changes. Documents are often professional reports in PDF, approval documents or instructions in Word, and some structured data files like Excel spreadsheets. Fields and units are highly specialized. Examples include "half-life" in hours (h), "peak plasma concentration" in nanograms per milliliter (ng/mL), "dissolution rate" as a percentage (%), and "impurity content" in parts per million (ppm).

Constraints on HTTP Interface and External Systems

The specialized nature of DTP pharmacy registration data requires external system interfaces to have robust text parsing and information extraction capabilities. These capabilities must accurately identify critical fields such as drug names, ingredients, dosages, indications, and adverse reactions. Low data update frequency means real-time requirements for the interface are not high. However, data completeness and consistency are critical. Diverse document formats, especially numerous unstructured PDF and Word documents, necessitate HTTP interfaces that support file uploads and can invoke backend services for content parsing. Recognizing specialized fields and units requires external systems to have domain-specific knowledge graphs or custom entity recognition capabilities to ensure data integrity during transmission and processing. The strictness of drug regulatory requirements demands highly robust data validation and error handling mechanisms in interface responses to prevent submission failures due to data errors.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	PDF and Word documents in registration data, especially those with many charts, can be large.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large files and OCR recognition can be time-consuming, requiring sufficient timeout to prevent interruptions.
`text_embedding_model`	`text-embedding-ada-002`	Suitable for semantic understanding and vectorization of specialized biomedical texts.
`max_tokens`	`8192`	Individual paragraphs or summaries in registration documents can be long, requiring support for longer contexts.
`similarity_threshold`	`0.75`	Ensures retrieved data is highly relevant to the query, avoiding the introduction of inaccurate information.
`response_format`	`json`	Facilitates structured data processing and integration by external systems.

Common Pitfalls

Incorrect Content-Type setting when calling external APIs to upload files. This leads to file parsing failures or 400 Bad Request errors.
Key fields like drug batch number or production date are empty after parsing unstructured documents. This occurs due to insufficient OCR accuracy or lack of preprocessing for specific document templates.
External systems return 504 Gateway Timeout errors. This happens when parsing large PDF files or performing complex semantic analysis exceeds the interface's default timeout limit.

Verification Steps

Upload a typical drug instruction manual in PDF format. Check if the parsing result accurately extracts core fields like drug name, indications, and dosage. Verify the completeness of the extracted fields.
Use an API call workflow to upload an Excel file containing multi-page tables. Verify that table data is correctly recognized and converted into a queryable structure. Check key numerical values and units.
Simulate a query containing specialized terminology. Check the relevance of the retrieved results. Compare them with the original registration data to confirm the similarity threshold is appropriate.
Review system logs. Confirm that no timeout or out-of-memory errors occur when processing large files or complex queries.

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.