NEW PROJ: Local RAG Agent with Qwen 3 and Gemma 3 Models

rag_tutorials/qwen_local_rag/README.md

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+# 🐋 Qwen 3 Local RAG Reasoning Agent
+
+This RAG Application demonstrates how to build a powerful Retrieval-Augmented Generation (RAG) system using locally running Qwen 3 and Gemma 3 models via Ollama. It combines document processing, vector search, and web search capabilities to provide accurate, context-aware responses to user queries.
+
+## Features
+
+- **🧠 Multiple Local LLM Options**:
+
+  - Qwen3 (1.7b, 8b) - Alibaba's latest language models
+  - Gemma3 (1b, 4b) - Google's efficient language models with multimodal capabilities
+  - DeepSeek (1.5b) - Alternative model option
+- **📚 Comprehensive RAG System**:
+
+  - Upload and process PDF documents
+  - Extract content from web URLs
+  - Intelligent chunking and embedding
+  - Similarity search with adjustable threshold
+- **🌐 Web Search Integration**:
+
+  - Fallback to web search when document knowledge is insufficient
+  - Configurable domain filtering
+  - Source attribution in responses
+- **🔄 Flexible Operation Modes**:
+
+  - Toggle between RAG and direct LLM interaction
+  - Force web search when needed
+  - Adjust similarity thresholds for document retrieval
+- **💾 Vector Database Integration**:
+
+  - Qdrant vector database for efficient similarity search
+  - Persistent storage of document embeddings
+
+## How to Get Started
+
+### Prerequisites
+
+- [Ollama](https://ollama.ai/) installed locally
+- Python 3.8+
+- Qdrant account (free tier available) for vector storage
+- Exa API key (optional, for web search capability)
+
+### Installation
+
+1. Clone the GitHub repository
+
+```bash
+git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
+cd rag_tutorials/qwen_local_rag
+```
+
+2. Install the required dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+3. Pull the required models using Ollama:
+
+```bash
+ollama pull qwen3:1.7b # Or any other model you want to use
+ollama run snowflake-arctic-embed # Or any other model you want to use
+```
+
+4. Get your API keys:
+
+   - Qdrant API key and URL (for vector database)
+   - Exa API key (optional, for web search)
+5. Run the application:
+
+```bash
+streamlit run qwen_local_rag_agent.py
+```
+
+## How It Works
+
+1. **Document Processing**:
+
+   - PDF files are processed using PyPDFLoader
+   - Web content is extracted using WebBaseLoader
+   - Documents are split into chunks with RecursiveCharacterTextSplitter
+2. **Vector Database**:
+
+   - Document chunks are embedded using Ollama's embedding models
+   - Embeddings are stored in Qdrant vector database
+   - Similarity search retrieves relevant documents based on query
+3. **Query Processing**:
+
+   - User queries are analyzed to determine the best information source
+   - System checks document relevance using similarity threshold
+   - Falls back to web search if no relevant documents are found
+4. **Response Generation**:
+
+   - Local LLM (Qwen/Gemma) generates responses based on retrieved context
+   - Sources are cited and displayed to the user
+   - Web search results are clearly indicated when used
+
+## Configuration Options
+
+- **Model Selection**: Choose between different Qwen, Gemma, and DeepSeek models
+- **RAG Mode**: Toggle between RAG-enabled and direct LLM interaction
+- **Search Tuning**: Adjust similarity threshold for document retrieval
+- **Web Search**: Enable/disable web search fallback and configure domain filtering
+
+## Use Cases
+
+- **Document Q&A**: Ask questions about your uploaded documents
+- **Research Assistant**: Combine document knowledge with web search
+- **Local Privacy**: Process sensitive documents without sending data to external APIs
+- **Offline Operation**: Run advanced AI capabilities with limited or no internet access
+
+## Requirements
+
+See `requirements.txt` for the complete list of dependencies.