RAG Agent with Cohere ⌘R

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.

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
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
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:

git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd rag_tutorials/rag_agent_cohere

2. Install dependencies:

pip install -r requirements.txt

streamlit run rag_agent_cohere.py