Generate responses to your queries
Generate detailed step-by-step answers to questions
Talk to a language model
Generate text and speech from audio input
Chat with Qwen2-72B-instruct using a system prompt
Chat with a Qwen AI assistant
customizable ChatBot API + UI
Generate code and answers with chat instructions
Fast and free uncensored chatbot that just works.
Run Llama,Qwen,Gemma,Mistral, any warm/cold LLM. No GPU req.
Compare chat responses from multiple models
Generate conversational responses to text input
Chatbot
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.