{"repo":"husaynirfan1/simple-rag","free":true,"listed":false,"github":"https://github.com/husaynirfan1/simple-rag","clone":"git clone https://github.com/husaynirfan1/simple-rag.git","description":"Simple RAG system powered by Milvus.","language":"Python","stars":20,"topics":["milvus","vector","vector-database","deepseek-r1","llama3-1","llm","python","qwen2-5","nltk","hybridsearch"],"license":"MIT","category":"ai-agents","readme_excerpt":"Simple RAG 📌 A not-so-lightweight Retrieval-Augmented Generation (RAG) system utilizing Milvus (Zilliz Cloud) as a vector database. This project uses scraped Al-Manar News as sample data. 🚀 Features - Coreference Resolution : Uses lingmesscoref to resolve chat history. - Multi-turn Conversation : Customizable number of window turns. - Web Scraping : Includes a scraping script for Al-Manar English. - Scalability : Utilizes Milvus and Zilliz Cloud , making scaling easy. 🛠 Installation 1️⃣ Set Up Milvus (Zilliz Cloud Recommended) You can either build your own Milvus database or use Zilliz Cloud for convenience. The schema in insertDataChunks.py should look like this (for reference, it is already commented in the script): 2️⃣ Install Dependencies 3️⃣ Install & Configure Ollama This project uses Llama 3.1 8B for contextual chunking and Qwen2.5 14B for user interaction. You can modify these in insertDataChunks.py and the Streamlit streamlit app.py script. 4️⃣ Install NLTK This project uses NLTK for text chunking. After chunking, context is injected into the chunks, ensuring that the uploaded vectorized hybrid (sparse and dense) data includes both the original text and contextual chunks. 5️⃣ Process and Insert Data Run insertDataChunks.py. It will ask to input path of folder. Use folder path in the current repo for testing. 6️⃣ Run Streamlit App 🔄 Flowchart The project initially utilized DeepSeek R1 for its reasoning capabilities. However, for general use, it is recommended to u","default_branch":null,"files":null,"tree":[],"storefront":"/r/husaynirfan1","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/husaynirfan1/simple-rag/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}