Main

.env.example

@@ -0,0 +1,5 @@
+# This is an example environment file.
+# Copy this file to .env and fill in your actual values.
+
+# Get your API key from https://platform.openai.com/account/api-keys
+OPENAI_API_KEY="sk-..."

.github/workflows/ci.yml

@@ -38,5 +38,8 @@ jobs:
 
       # Step 5: Run the unit tests with Pytest
       - name: Test with Pytest
+        # Add this 'env' block to inject the secret
+        env:
+          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
         run: |
           pytest

README.md

@@ -0,0 +1,120 @@
+# Reflection Agent Service 🤖✨
+
+[![Code Quality and Tests](https://github.com/PRYSKAS/REFLECTION_PATTERN_AGENT/actions/workflows/ci.yml/badge.svg)](https://github.com/PRYSKAS/REFLECTION_PATTERN_AGENT/actions)
+
+This project is an AI microservice built around the **Reflection agent design pattern** to iteratively analyze, critique, and refine Large Language Model (LLM) outputs. It showcases the end-to-end engineering process of transforming a core AI script into a robust, containerized, and production-ready service.
+
+## 🧠 Core Concept: The Reflection Pattern
+
+The Reflection Pattern enhances the quality and reliability of LLM outputs through a structured, three-step self-critique process:
+
+1.  **Generate:** The agent produces an initial draft in response to a prompt.
+2.  **Reflect:** The agent analyzes its own draft, identifies flaws or areas for improvement, and generates a list of actionable, constructive critiques.
+3.  **Refine:** The agent re-attempts the original task, this time using its own critiques as a guide to generate a superior final output.
+
+This cycle mimics the human process of drafting and revision, leading to responses that are more coherent, accurate, and aligned with the user's intent.
+
+## 🚀 Engineering & MLOps Highlights
+
+This project emphasizes the engineering required to serve an AI model reliably and scalably.
+
+* **Microservice API:** The agent's logic is exposed via a RESTful API using **FastAPI**, with clear data contracts enforced by **Pydantic** for robust I/O validation.
+* **Containerization:** The entire application is containerized with **Docker**, ensuring a consistent execution environment and simplifying deployment across any platform.
+* **Unit Testing:** The agent's core business logic is rigorously tested using **Pytest** and **pytest-mock**, guaranteeing the reliability and integrity of each component.
+* **Automated CI/CD:** A **GitHub Actions** pipeline is triggered on every push to `main`, automatically performing:
+    * **Linting** with **Ruff** to enforce code quality and style consistency.
+    * **Unit Testing** to prevent regressions and ensure code health.
+* **Secrets Management:** API keys and sensitive credentials are handled securely using `.env` files for local development and **GitHub Actions Secrets** in the CI/CD pipeline, preventing any exposure in the repository.
+
+## 🏗️ Service Architecture
+
+```mermaid
+graph TD
+    A[User/Client] -->|HTTP POST Request| B(FastAPI Service);
+    B -->|prompt| C{ReflectionAgent};
+    C -->|1. Generate Draft| D[OpenAI API];
+    D -->|Draft| C;
+    C -->|2. Generate Reflections| D;
+    D -->|Reflections| C;
+    C -->|3. Generate Final Output| D;
+    D -->|Final Output| C;
+    C -->|Complete Response| B;
+    B -->|JSON Response| A;
+```
+
+## 🏁 Getting Started
+
+### Prerequisites
+* Git
+* Python 3.9+
+* Docker Desktop (running)
+
+### 1. Running Locally (for Development)
+
+1.  **Clone the repository:**
+    ```bash
+    git clone [https://github.com/PRYSKAS/REFLECTION_PATTERN_AGENT.git](https://github.com/PRYSKAS/REFLECTION_PATTERN_AGENT.git)
+    cd REFLECTION_PATTERN_AGENT
+    ```
+
+2.  **Set up the environment:**
+    * Create a `.env` file from the example: `copy .env.example .env` (on Windows) or `cp .env.example .env` (on Unix/macOS).
+    * Add your `OPENAI_API_KEY` to the new `.env` file.
+
+3.  **Install dependencies:**
+    ```bash
+    pip install -r requirements.txt
+    pip install -e .
+    ```
+
+4.  **Run tests to verify the setup:**
+    ```bash
+    pytest
+    ```
+
+5.  **Start the API server:**
+    ```bash
+    uvicorn main:app --reload --port 8001
+    ```
+    The API will be available at `http://127.0.0.1:8001/docs`.
+
+### 2. Running with Docker (Production Mode)
+
+This is the recommended way to run the service for a stable, isolated deployment.
+
+1.  **Build the Docker image:**
+    ```bash
+    docker build -t reflection-agent-service .
+    ```
+
+2.  **Run the container:**
+    ```bash
+    docker run -d -p 8001:8001 --env-file .env --name reflection-agent reflection-agent-service
+    ```
+    The service will now be running in the background. Access the API documentation at `http://127.0.0.1:8001/docs`.
+
+## 📡 API Endpoint
+
+### `POST /run`
+
+Executes the agent's full generate-reflect-generate cycle.
+
+**Request Body:**
+```json
+{
+  "prompt": "Write a tweet about the importance of CI/CD in AI engineering."
+}
+```
+
+**Success Response (200 OK):**
+```json
+{
+  "initial_draft": "CI/CD is crucial in AI engineering. #AI #MLOps",
+  "reflections": [
+    "- The tweet is too short and generic.",
+    "- It could add a specific benefit, like 'accelerating value delivery'.",
+    "- An emoji would increase engagement."
+  ],
+  "final_output": "🚀 CI/CD in AI Engineering isn't a luxury; it's a necessity! It accelerates value delivery by automating testing and deployment, ensuring robust models reach production faster. #MLOps #AIEngineering"
+}
+```

lab_reflection_agent.ipynb

@@ -19,7 +19,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
     "from dotenv import load_dotenv\n",
     "\n",
     "# Import our custom agent class\n",

main.py

@@ -4,12 +4,12 @@
 from pydantic import BaseModel, Field
 from typing import List
 
-# Load environment variables from a .env file
-load_dotenv()
-
 # Import core logic
 from reflection_pattern_agent.reflection_agent import ReflectionAgent
 
+# Load environment variables from a .env file
+load_dotenv()
+
 # Set up logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 logger = logging.getLogger(__name__)

tests/test_reflection_agent.py

@@ -1,4 +1,3 @@
-import pytest
 from reflection_pattern_agent.reflection_agent import ReflectionAgent
 
 # Create a fake response object that looks exactly like the real OpenAI response.