Integrating HTTP Interfaces and External Systems for Phase

Data Characteristics

Phase II-III clinical trial quality documents originate from various sources. These include clinical trial protocols, investigator brochures, informed consent forms, ethics approval documents, case report forms (CRF/eCRF), laboratory test reports, imaging data, statistical analysis plans and reports, and adverse event (AE/SAE) reports. Documents are typically stored in formats like PDF, DOCX, and XLSX. Some data may exist as structured database records.

Document updates occur continuously during a trial. Protocol amendments, CRF entries, and AE/SAE reports are examples of ongoing updates. Statistical analysis reports generate at specific milestones. Document structures are complex and contain extensive specialized terminology and abbreviations. Fields and units involve dosage (mg/kg), time points (hours/days), biomarkers (ng/mL), and efficacy indicators (percentage change, absolute values). Different documents may use varying expressions for the same concept.

Constraints from HTTP Interfaces and External Systems

The complexity and diversity of Phase II-III clinical quality documents impose specific requirements on HTTP interfaces and external system integration.

First, diverse document formats require interfaces to support multiple file types for upload and parsing. Interfaces must accurately extract key information from unstructured text. Second, continuous updates necessitate incremental synchronization and version management capabilities to ensure the knowledge base's timeliness. For example, new adverse event reports must update the knowledge base promptly. Specialized terminology, abbreviations, and varied expressions in documents require standardization and entity recognition during data preprocessing to improve retrieval and question-answering accuracy. Finally, the strictness of fields and units means interfaces must maintain data type and unit consistency during data transfer and storage. This prevents errors from unit conversions or data type mismatches, which is critical when integrating with external database systems.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`4096 tokens`	Ensures sufficient context for complex clinical trial protocols or investigator brochures.
`UPLOAD_FILE_MAX_SIZE`	`200 MB`	Accommodates large imaging reports or PDF documents with extensive charts.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Provides ample time for parsing large, complex clinical documents.
`Chunk size`	`800–1200 characters`	Balances context completeness and retrieval efficiency, adapting to clinical document paragraph structures.
`Similarity threshold`	`0.75`	Improves recall accuracy for specialized terms and key information, reducing irrelevant results.
`API_KEY_CHECK_MODE`	`strict`	Enforces strict authentication for external system calls, ensuring data security.

Common Pitfalls

An {"code":514,"statusText":"unAuthApiKey","message":"common:code_error.e error after an API call indicates an invalid or expired API_KEY in the Authorization request header.
Uploading a large clinical trial protocol PDF results in no knowledge base update and no error messages. This usually means PARSE_FILE_TIMEOUT_SECONDS is too low, causing file parsing to time out.
API retrieval fails to recall relevant adverse event reports, even though logs show the knowledge base updated. This happens when Similarity threshold (similarity threshold) is set too high, filtering out semantically similar but not perfectly matching documents.

Verification Steps

Upload a Phase II-III clinical trial protocol PDF containing specialized terminology. Call the /api/v1/chat/completions API to verify accurate answers based on the document content.
Call the API multiple times with different chatId parameters. Check the conversation logs in the FastGPT backend to confirm each conversation correctly links to its chatId.
Simulate an external system submitting a new adverse event report via the HTTP interface. Immediately use the retrieval interface to verify the report content is retrievable. Check if Recall count (number of recalled items) meets expectations.

Note: The values provided are common starting points. Measure them against specific samples to determine optimal settings.

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.