simple-rag
# 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): ```python fields = [ # FieldSchema( # name="pk", dtype=DataType.VARCHAR, is_primary=True, auto_id=True, max_length=100 # ), # FieldSchema(name="text", dtype=DataType.VARCHAR, max_length=65535), # Store original text # FieldSchema(name="sparse_vector", dtype=DataType.SPARSE_FLOAT_VECTOR), # Sparse vector field # FieldSchema(name="dense_vector", dtype=DataType.FLOAT_VECTOR, dim=dense_dim), # Dense vector field with 768 dimensions # ) ] ``` ### 2️⃣ Install Dependencies ```sh pip install -r requirements.txt ``` ### 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. ```sh pip install nltk ``` ### 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. ```python python insertDataChunks.py Path: /folder ``` ### 6️⃣ Run Streamlit App ```sh streamlit run streamlit_app.py ``` ## 🔄 Flowchart *The project initially utilized DeepSeek R1 for its reasoning capabilities. However, for general use, it is recommended to use a non-reasoning model.*  ## 💡 Notes - You will see **handle_message** twice in the Streamlit app. This is because the project was initially designed for a **Telegram bot** as the interface. - The repository also includes a **pre-written Telegram bot script** if you want to use it. --- ## 📌 License This project is open-source under the **MIT License**. ## 🔗 Links - [Zilliz Cloud](https://zilliz.com/cloud) - [Milvus](https://milvus.io/) - [Ollama](https://ollama.com/) - [NLTK](https://www.nltk.org/) - [Project Repository](https://github.com/husaynirfan1/simple-rag) --- ## 📧 Contact   - **Gmail**: [[email protected]](mailto:[email protected]) - **GitHub Issues**: [Open an issue](https://github.com/husaynirfan1/simple-rag/issues) - **Linked In**: [Husayn Irfan](https://my.linkedin.com/in/husayn-irfan-7b4103258)