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

Go to Cohere Platform
Sign up or log in to your account
Navigate to API Keys section
Create a new API key

2. Qdrant Cloud Setup

Visit Qdrant Cloud
Create an account or sign in
Create a new cluster
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

Clone the repository:

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

Install dependencies:

pip install -r requirements.txt

streamlit run rag_agent_cohere.py

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