Скачать или смотреть RAG-LLM API Automation Framework

RAG stands for Retrieval-Augmented Generation, an AI framework that enhances a Large Language Model's (LLM) capabilities by combining it with an external information retrieval system

This project is a Streamlit-powered dashboard + CLI framework for automated API testing using OpenAPI specifications.
It generates test cases (headers, parameters, body validation, security checks, boundary tests) and validates responses with both rule-based checks and LLM-based validation.

GitHub

https://github.com/Appmakers123/LLM-A...


Live demo prototype
https://llm-api-testing-framework-pyo...

✨ Features
📑 Parse OpenAPI spec and auto-generate test cases
🛠️ API test execution (CLI or Streamlit UI)
✅ Validations for:
Headers
Parameters
Body schema (required, types, enums, boundaries, combinations)
Security checks (e.g., token)
🤖 LLM Validation (via Perplexity API)
📊 CSV reports with pass/fail results
💬 Knowledge Base Chat (ask questions against docs/PDFs)
🗂 Project Structure
LLM-API-Testing/

│── app.py # Streamlit Dashboard

│── main.py # CLI Test Runner

│── config.py # Configurations (⚠️ don't commit secrets!)

│── requirements.txt # Python dependencies

│── README.md # Project documentation

├── openapi/ # OpenAPI loaders & helpers

├── tests/ # Test generators & executors

├── knowledgebase/ # Knowledge base loader

├── utils/ # Utility functions (file/text handling)

├── reports/ # Test execution CSV reports

└── testdata/ # API spec YAML/JSON files

⚙️ Installation
Clone the repo and install dependencies:

pip install -r requirements.txt
Run with Streamlit Dashboard

streamlit run app.py
Run with CLI

python main.py --run-tests