HTTP Interface and External Systems for Peptide Drug

Data Characteristics

Peptide drug pharmacovigilance data comes primarily from clinical trial reports, real-world evidence (RWE), post-market surveillance, and patient reports. This data often combines structured formats (e.g., database records) and unstructured formats (e.g., free text, PDF documents). Structured data includes patient demographics, medication history, adverse event (AE) codes (e.g., MedDRA terms), dosage, administration routes, and event onset and resolution times. Unstructured data contains detailed case descriptions, physician diagnoses, and laboratory test results. Data updates frequently, especially during post-market surveillance, as new adverse event reports continuously arrive. Documents often span multiple pages. Field names may vary across reporting sources, such as "adverse reaction" or "side effect." Dosage units include milligrams (mg), micrograms (μg), and international units (IU), often accompanied by complex modifiers.

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

The complexity of peptide drug data sources requires HTTP interfaces with robust data parsing capabilities to handle diverse data formats. High-frequency data updates mean interfaces must support real-time or near real-time incremental synchronization to minimize data latency. The presence of unstructured data, particularly detailed case descriptions, demands higher quality from text embedding models to ensure accurate semantic understanding. Multi-page documents and inconsistent field names require external systems to offer flexible configuration of mapping rules and preprocessing steps for data extraction and standardization. The complexity of dosage units can lead to unit conversion errors, necessitating strict validation by the interface after data reception. Furthermore, the use of specialized terminology like MedDRA codes increases the difficulty of data preprocessing. This requires integrating dedicated medical dictionary services for term mapping and standardization to prevent information loss or misinterpretation.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`maxContext`	`2000 characters`	Balances the completeness of complex case descriptions with model processing efficiency, preventing truncation of overly long text.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Peptide drug pharmacovigilance reports are often multi-page PDFs; parsing takes longer, so sufficient time is allocated.
`Chunk size`	`500 characters`	Ensures each segment contains enough context for semantic understanding while avoiding excessively long segments.
`Recall count`	`Top 10 entries`	Considers the potential correlations of peptide drug adverse events, expanding the recall range to capture more relevant information.
`Similarity threshold`	`0.75–0.85`	Balances accuracy and recall, reducing false positives while not missing critical adverse events.
`Rerank result count`	`Top 5 entries`	Further optimizes relevance through a reranking model based on initial recall, improving the quality of final results.

Common Pitfalls

HTTP interface returns 400 Bad Request errors because the incoming JSON structure does not match expectations, often due to mismatched date formats or enum values.
Knowledge base file uploads remain in "processing" status for extended periods, failing to parse successfully. This usually occurs when embedded fonts or encryption in PDF documents prevent the parser from correctly reading content.
Peptide dosage fields imported by external systems are empty or contain abnormal values. This happens when unit conversion rules are not correctly configured, leading to confusion between different units like milligrams and micrograms.

Verification Steps

On the FastGPT console's "Data Source" page, review the latest HTTP interface synchronization records. Check for a Success status and confirm that the data volume meets expectations.
Upload a typical PDF document containing a peptide drug adverse event report. Observe whether the knowledge base processing completes normally and verify that embedded segment content is complete and free of garbled characters.
Using FastGPT's "Test" feature, query a known adverse reaction case. Check if the returned results accurately mention the relevant peptide drug, dosage, and adverse event, and cross-reference with the original data to confirm information accuracy.

The values provided 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.