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*Mini-RAG*\n\n\u003cp align=\"center\"\u003e \u003cimg src=\"https://img.shields.io/badge/FastAPI-005571?style=for-the-badge\u0026logo=fastapi\u0026logoColor=white\" alt=\"FastAPI\" /\u003e \u003cimg src=\"https://img.shields.io/badge/OpenAI-412991?style=for-the-badge\u0026logo=openai\u0026logoColor=white\" alt=\"OpenAI\" /\u003e \u003cimg src=\"https://img.shields.io/badge/Cohere-39594D?style=for-the-badge\u0026logo=cohere\u0026logoColor=white\" alt=\"Cohere\" /\u003e \u003cimg src=\"https://img.shields.io/badge/PostgreSQL-4169E1?style=for-the-badge\u0026logo=postgresql\u0026logoColor=white\" alt=\"PostgreSQL\" /\u003e \u003cimg src=\"https://img.shields.io/badge/PgVector-336791?style=for-the-badge\u0026logo=postgresql\u0026logoColor=white\" alt=\"PgVector\" /\u003e \u003cimg src=\"https://img.shields.io/badge/Qdrant-FF4B4B?style=for-the-badge\u0026logo=qdrant\u0026logoColor=white\" alt=\"Qdrant\" /\u003e \u003cimg src=\"https://img.shields.io/badge/Nginx-009639?style=for-the-badge\u0026logo=nginx\u0026logoColor=white\" alt=\"Nginx\" /\u003e \u003cimg src=\"https://img.shields.io/badge/Prometheus-E6522C?style=for-the-badge\u0026logo=prometheus\u0026logoColor=white\" alt=\"Prometheus\" /\u003e \u003cimg src=\"https://img.shields.io/badge/Grafana-F46800?style=for-the-badge\u0026logo=grafana\u0026logoColor=white\" alt=\"Grafana\" /\u003e \u003c/p\u003e \u003cp align=\"center\"\u003e \u003cimg src=\"https://img.shields.io/github/license/mahmoudm13/mini-rag?style=flat-square\" alt=\"License\" /\u003e \u003c/p\u003e\n\nMini-RAG is a streamlined, scalable implementation of a Retrieval-Augmented Generation pipeline. It bridges the gap between static LLMs and dynamic private data by integrating high-performance vector databases (Qdrant \u0026 PgVector) with a robust FastAPI backend. Designed with observability in mind, it includes a full monitoring stack to track performance and system health in real-time.\n\n\n## Requirements\n\n- Python 3.8 or later\n\n### Install Dependencies\n```bash\nsudo apt update\nsudo apt install libpq-dev gcc python3-dev\n```\n\n\n### Install Python using MiniConda\n\n1) Download and install MiniConda from [here](https://docs.anaconda.com/free/miniconda/#quick-command-line-install).\n2) Create a new environment using the following command:\n```bash\n$ conda create -n mini-rag python=3.8\n```\n3) Activate the environment:\n\n```bash\n$ conda activate mini-rag\n```\n\n### (Optional) Setup your command line for better readability\n```bash\nexport PS1=\"\\[\\033[01;32m\\]\\u@\\h:\\w\\n\\[\\033[00m\\]\\$ \"\n```\n\n## Installation\n\n### Install the required packages\n\n```bash\n$ cd src/\n$ pip install -r requirements.txt\n```\n\n### Setup the environment variables and Run Docker Compose Services\n\n```bash\n$ cd ../docker/env\n$ cp .env.example.app .env.app\n$ cp .env.example.postgres .env.postgres\n$ cp .env.example.grafana .env.grafana\n$ cp .env.example.postgres-exporter .env.postgres-exporter\n```\n\nupdate `.env` files with your credientials\n\n```bash\n$ cd ..\n$ sudo docker compose up --build -d\n```\n\n## Access Services\n\n- FastAPI: http://localhost:8000\n- Grafana: http://localhost:3000\n- Prometheus: http://localhost:9090\n\n## Run the FastAPI server (Development Mode)\n```bash\n$ uvicorn main:app --reload --host 0.0.0.0 --port 5000\n``` \n\n## POSTMAN Collection\nDownload the POSTMAN collection from [/src/assets/mini-rag-app.postman_collection.json](/src/assets/mini-rag-app.postman_collection.json)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmahmoudm13%2Fmini-rag","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmahmoudm13%2Fmini-rag","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmahmoudm13%2Fmini-rag/lists"}