Generate responses to your queries
Engage in chat conversations
Generate chat responses using Llama-2 13B model
Chat with a Qwen AI assistant
Engage in chat with Llama-2 7B model
This Chatbot for Regal Assistance!
ChatBot Qwen
Generate chat responses with Qwen AI
Engage in conversations with a smart AI assistant
Interact with NCTC OSINT Agent for OSINT tasks
Communicate with a multimodal chatbot
Send messages to a WhatsApp-style chatbot
Chat with GPT-4 using your API key
DeployPythonicRAG is a Python-based framework designed to deploy and manage Retrieval-Augmented Generation (RAG) models. It provides a straightforward way to integrate and query AI models for generating responses to user inputs, making it a powerful tool for building and deploying chatbot applications.
• RAG Model Support: Seamlessly integrates with state-of-the-art RAG models to enhance response generation. • Customizable Responses: Allows fine-tuning of model parameters to align with specific use cases. • Scalability: Designed to handle multiple queries efficiently, making it suitable for large-scale applications. • User-Friendly API: Provides an intuitive interface for developers to interact with the model.
pip install deploy-pythonic-rag to install the library.from deploy_pythonic_rag import RAGModel in your Python script.model = RAGModel().response = model.query("your input here").What is RAG?
RAG (Retrieval-Augmented Generation) is a technique that combines retrieval of relevant information with generation to produce more accurate and context-aware responses.
Do I need deep technical knowledge to use DeployPythonicRAG?
No, DeployPythonicRAG is designed to be user-friendly. It abstracts complex functionalities, allowing developers to focus on integrating the model without needing extensive AI expertise.
Where can I find more documentation?
Detailed documentation and examples can be found on the official DeployPythonicRAG GitHub repository.