New proj with cohere - rag + agent

rag_tutorials/rag_agent_cohere/README.md

@@ -0,0 +1,68 @@
+# RAG Agent with Cohere 🤖
+
+A RAG Agentic system built with Cohere's new model Command-r7b-12-2024, Qdrant for vector storage, Langchain for RAG and LangGraph for orchestration. This application allows users to upload documents, ask questions about them, and get AI-powered responses with fallback to web search when needed.
+
+## Demo
+
+
+
+## Features
+
+- **Document Processing**
+  - PDF document upload and processing
+  - Automatic text chunking and embedding
+  - Vector storage in Qdrant cloud
+
+- **Intelligent Querying**
+  - RAG-based document retrieval
+  - Similarity search with threshold filtering
+  - Automatic fallback to web search when no relevant documents found
+  - Source attribution for answers
+
+- **Advanced Capabilities**
+  - DuckDuckGo web search integration
+  - LangGraph agent for web research
+  - Context-aware response generation
+  - Long answer summarization
+
+- **Model Specific Features**
+  - Command-r7b-12-2024 model for Chat and RAG
+  - cohere embed-english-v3.0 model for embeddings
+  - create_react_agent function from langgraph 
+  - DuckDuckGoSearchRun tool for web search
+
+## Prerequisites
+
+### 1. Cohere API Key
+1. Go to [Cohere Platform](https://dashboard.cohere.ai/api-keys)
+2. Sign up or log in to your account
+3. Navigate to API Keys section
+4. Create a new API key
+
+### 2. Qdrant Cloud Setup
+1. Visit [Qdrant Cloud](https://cloud.qdrant.io/)
+2. Create an account or sign in
+3. Create a new cluster
+4. Get your credentials:
+   - Qdrant API Key: Found in API Keys section
+   - Qdrant URL: Your cluster URL (format: `https://xxx-xxx.aws.cloud.qdrant.io`)
+
+
+## How to Run
+
+1. Clone the repository:
+```bash
+git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
+cd rag_tutorials/rag_agent_cohere
+```
+
+2. Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+```bash
+streamlit run rag_agent_cohere.py
+```
+
+