HTTP Interface and External Systems for Real-World Evidence

Data Characteristics for This Category

Real-World Evidence (RWE) regulatory data primarily originates from guidelines, Standard Operating Procedures (SOPs), internal management documents issued by regulatory bodies, and ethics review documents submitted by research institutions. This data updates infrequently, typically changing with policy adjustments or industry standard updates, which can be every few months or even years. Document structures are mostly unstructured text, such as PDF regulatory files, Word document SOPs, or Excel spreadsheet appendices. Content covers various stages, including research design, data collection, statistical analysis, and results reporting. Fields include research protocol number, version number, revision date, scope, responsible person, operating procedures, and risk management measures. Some fields may contain medical terminology or specific regulatory codes.

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

The low update frequency of RWE regulatory data means that real-time data synchronization with external systems is not necessary; periodic batch synchronization is more appropriate, for example, daily or weekly. Most documents are unstructured text, which requires FastGPT's file parsing capabilities to accurately identify and extract text content from PDFs and Word documents. The regulations contain extensive specialized terminology and regulatory codes, demanding strong domain understanding from the model to accurately match and explain during Q&A. Fields like version number and revision date are critical for regulation validity. When querying via HTTP interface, support for filtering by version number or date is essential to ensure retrieval of the latest or specific historical versions. Additionally, due to sensitive compliance information, external systems calling the HTTP interface must implement strict authentication and access control to prevent unauthorized access.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`maxContext`	`4000` tokens	Regulatory documents are often long; a larger context window is needed to accommodate key information.
`Chunk size` (Segment Length)	`800` characters	Balances semantic completeness and retrieval efficiency, avoiding excessive fragmentation or overly long segments.
`Recall count` (Recall Count)	`8` items	Ensures coverage of multiple aspects of relevant regulations, improving answer accuracy.
`Similarity threshold` (Similarity Threshold)	`0.75`	Ensures strong relevance of recalled content, filtering out inaccurate or irrelevant information.
`Rerank result count` (Reranked Return Count)	`4` items	Selects the most relevant few items for in-depth processing, optimizing the final answer.
`PARSER_TIMEOUT`	`60` seconds	Processing complex PDF or Word documents may require a longer parsing time.

Three Common Mistakes

An HTTP interface call returning a 403 Forbidden status code typically indicates an incorrectly configured API Key or insufficient permissions.
An empty or inaccurate answer after an interface call may be due to document parsing failure, preventing effective storage of regulatory text in the knowledge base.
When querying a specific regulation version, the system always returns the latest version. This indicates missing version number or date filter fields in the HTTP request parameters, or incorrect parsing of these fields.

How to Verify Correct Setup

Upload typical RWE regulatory PDF or Word documents via the FastGPT interface and check if the files are successfully parsed and searchable knowledge segments are generated.
Use FastGPT's API debugging tool to simulate external system calls, input several key RWE regulation-related questions, and check the accuracy and completeness of the returned results.
When calling the HTTP interface, try passing parameters with different version numbers or revision dates to verify that the system can accurately retrieve the corresponding version of the regulatory content.
After integrating FastGPT's HTTP interface into an external system, conduct end-to-end testing to ensure a smooth process from user query to answer retrieval.

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.