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