A curated archive of 313 AI prompts for ChatGPT, Claude, Gemini, and anywhere else you need them. Search, filter by category, and copy with one click.
Lists Python package dependencies including OpenAI agents, Streamlit, Pydantic, and utilities.
openai-agents openai streamlit uuid pydantic python-dotenv asyncio
Streamlit app that generates MP3 music tracks using ModelsLab API and GPT-4 from text prompts.
## ModelsLab Music Generator This is a Streamlit-based application that allows users to generate music using the ModelsLab API and OpenAI's GPT-4 model. Users can input a prompt describing the type of music they want to generate, and the application will generate a music track in MP3 format based on the given prompt. ## Features - **Generate Music**: Enter a detailed prompt for music generation
Specifies Python package dependencies with version constraints for an AI-powered Streamlit application.
agno>=2.2.10 Requests==2.32.3 streamlit==1.44.1 openai==2.8.1
Streamlit app converting blog URLs to audio podcasts using GPT-4, Firecrawl, and ElevenLabs APIs.
## 📰 ➡️ 🎙️ Blog to Podcast Agent This is a Streamlit-based application that allows users to convert any blog post into a podcast. The app uses OpenAI's GPT-4 model for summarization, Firecrawl for scraping blog content, and ElevenLabs API for generating audio. Users simply input a blog URL, and the app will generate a podcast episode based on the blog. ## Features - **Blog Scraping**: Scrapes th
Lists Python package dependencies with version constraints for an AI-powered application.
agno>=2.2.10 streamlit>=1.40.2 openai>=1.102.0 requests firecrawl-py>=4.6.0 elevenlabs>=1.0.0
A Streamlit app with specialized AI agents offering therapy, closure, routines, and honest breakup feedback.
### 🎓 FREE Step-by-Step Tutorial **👉 [Click here to follow our complete step-by-step tutorial](https://www.theunwindai.com/p/build-an-ai-breakup-recovery-agent-team-f29b) and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.** This is an AI-powered application designed to help users emotionally recover from breakups by providing support, guid
A pip requirements file listing specific Python package dependencies and their versions.
streamlit==1.44.1 pillow==11.1.0 agno>=2.2.10 google-genai==1.9.0 duckduckgo-search
Lists Python package requirements: streamlit, asyncio, and together.
streamlit asyncio together
Streamlit app implementing RAG with intelligent query routing across specialized databases and web search fallback.
### 🎓 FREE Step-by-Step Tutorial **👉 [Click here to follow our complete step-by-step tutorial](https://www.theunwindai.com/p/build-a-rag-agent-with-database-routing) and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.** A Streamlit application that demonstrates an advanced implementation of RAG Agent with intelligent query routing. The syst
Dependencies list for a LangChain, Qdrant, and Streamlit-based AI application.
langchain==0.3.12 langchain-community==0.3.12 langchain-core==0.3.28 qdrant-client==1.12.1 streamlit>=1.29.0 pypdf>=4.0.0 sentence-transformers>=2.2.2 agno langchain-openai==0.2.14 langgraph==0.2.53 duckduckgo-search==6.4.1
Multimodal RAG system using Cohere Embed-4 for image retrieval and Gemini 2.5 Flash for visual question answering.
A powerful visual Retrieval-Augmented Generation (RAG) system that utilizes Cohere's state-of-the-art Embed-4 model for multimodal embedding and Google's efficient Gemini 2.5 Flash model for answering questions about images and PDF pages. ## Features - **Multimodal Search**: Leverages Cohere Embed-4 to find the most semantically relevant image (or PDF page image) for a given text question. - **V
Specifies minimum versions for a Python project using Streamlit, Cohere, Google AI, and related libraries.
streamlit>=1.32.0 cohere>=5.0.0 google-generativeai>=0.3.0 Pillow>=10.0.0 requests>=2.31.0 numpy>=1.24.0 tqdm>=4.66.0 PyMuPDF>=1.23.0
Build an agentic RAG application using GPT-5, Agno framework, and LanceDB for semantic search and real-time Q&A.
### 🎓 FREE Step-by-Step Tutorial **👉 [Click here to follow our complete step-by-step tutorial](https://www.theunwindai.com/p/build-agentic-rag-with-openai-gpt-5) and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.** An agentic RAG application built with the Agno framework, featuring GPT-5 and LanceDB for efficient knowledge retrieval and qu
A requirements file listing Python dependencies for a Streamlit app with Agno, OpenAI, LanceDB, and dotenv.
streamlit agno>=2.2.10 openai lancedb python-dotenv
A framework-agnostic CLI clinic that classifies RAG pipeline bugs into 12 reusable failure patterns and suggests minimal structural fixes.
A small, framework-agnostic **RAG failure diagnostics clinic**. You paste a real bug description from your LLM + RAG pipeline. The script asks an LLM to classify the failure into one of several **reusable patterns** and suggests a **minimal structural fix** (not just “add more context” or “try a better model”). The goal is to show a pattern-driven way to debug RAG incidents that can be adapted
Specifies the minimum version requirement for the OpenAI Python package.
openai>=1.6.0
A multi-stage RAG system using LangGraph, Qdrant, Claude, and Tavily for self-correcting retrieval and generation.
A sophisticated Retrieval-Augmented Generation (RAG) system that implements a corrective multi-stage workflow using LangGraph. This system combines document retrieval, relevance grading, query transformation, and web search to provide comprehensive and accurate responses. ## Features - **Smart Document Retrieval**: Uses Qdrant vector store for efficient document retrieval - **Document Relevance
Python package requirements for a LangChain, LangGraph, Qdrant, and Streamlit AI application stack.
langchain==0.3.12 langgraph==0.2.53 qdrant-client==1.12.1 langchain-openai==0.2.14 langchain-anthropic==0.3.0 tavily-python==0.5.0 langchain-community==0.3.12 langchain-core==0.3.28 streamlit==1.41.1 tenacity==8.5.0 anthropic>=0.7.0 openai>=1.12.0 tiktoken>=0.6.0 pydantic>=2.0.0 numpy>=1.24.0 PyYAML>=6.0.0 nest-asyncio>=1.5.0
A document Q&A app using hybrid search, local LLMs, and RAGLite for accurate, context-aware answers via Streamlit.
A powerful document Q&A application that leverages Hybrid Search (RAG) and local LLMs for comprehensive answers. Built with RAGLite for robust document processing and retrieval, and Streamlit for an intuitive chat interface, this system combines document-specific knowledge with local LLM capabilities to deliver accurate and contextual responses. ## Demo: https://github.com/user-attachments/asse
A requirements.txt listing ML and RAG pipeline dependencies including LLaMA, Streamlit, and NLP libraries.
raglite==0.2.1 llama-cpp-python>=0.2.56 sentence-transformers>=2.5.1 pydantic==2.10.1 sqlalchemy>=2.0.0 psycopg2-binary>=2.9.9 pypdf>=3.0.0 python-dotenv>=1.0.0 rerankers==0.6.0 spacy>=3.7.0 streamlit>=1.31.0 flashrank==0.2.9 numpy>=1.24.0 pandas>=2.0.0 tqdm>=4.66.0
Streamlit app using Knowledge Graph RAG with Neo4j and Ollama for multi-hop reasoning and verifiable source citations.
A Streamlit application demonstrating how **Knowledge Graph-based Retrieval-Augmented Generation (RAG)** provides multi-hop reasoning with fully verifiable source attribution. ## 🎯 What Makes This Different? Traditional vector-based RAG finds similar text chunks, but struggles with: - Questions requiring information from multiple documents - Complex reasoning chains - Providing verifiable source
Python package requirements for a Streamlit app with Ollama and Neo4j integrations.
streamlit>=1.28.0 ollama>=0.1.0 neo4j>=5.0.0
JEE-level math tutor using Agentic-RAG with Qdrant, GPT-4.1, DSPy guardrails, and human feedback loop.
This project implements an **Agentic-RAG architecture** to simulate a math professor that solves **JEE-level math questions** with step-by-step explanations. The system smartly routes queries between a vector database and web search, applies input/output guardrails, and incorporates human feedback for continuous learning. ## 📌 Features - ✅ **Input Guardrails** (DSPy): Accepts only academic math
A requirements list for an AI application stack including OpenAI, LlamaIndex, Qdrant, DSPy, and Streamlit.
nest-asyncio openai==1.61.0 llama-index==0.12.33 llama-index-vector-stores-qdrant==0.6.0 qdrant-client==1.14.1 dspy==2.6.18 faiss-cpu==1.10.0 tavily-python==0.5.4 python-dotenv==1.1.0 streamlit==1.44.1 pandas==2.2.3 requests==2.32.3
Streamlit app to chat with any webpage using local Llama-3.1 and RAG, running fully offline.
## 💻 Local Lllama-3.1 with RAG Streamlit app that allows you to chat with any webpage using local Llama-3.1 and Retrieval Augmented Generation (RAG). This app runs entirely on your computer, making it 100% free and without the need for an internet connection. ### Features - Input a webpage URL - Ask questions about the content of the webpage - Get accurate answers using RAG and the Llama-3.1 mod
Lists Python package requirements for a Streamlit app using Ollama and LangChain integrations.
streamlit ollama langchain langchain_community langchain_ollama
Build a fully local RAG system using Llama 3.2, Qdrant vector DB, and Agno v2.0 with no external API dependencies.
## 🦙 Local RAG Agent with Llama 3.2 ### 🎓 FREE Step-by-Step Tutorial **👉 [Click here to follow our complete step-by-step tutorial](https://www.theunwindai.com/p/build-a-local-rag-agent) and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.** This application implements a Retrieval-Augmented Generation (RAG) system using Llama 3.2 via Ollama,
Specifies Python package dependencies including agno, qdrant-client, ollama, and pypdf.
agno>=2.2.10 qdrant-client ollama pypdf
RAG agentic system using Gemini 2.0, Qdrant vector storage, and Agno for intelligent document querying and web search fallback.
A RAG Agentic system built with the new Gemini 2.0 Flash Thinking model and gemini-exp-1206, Qdrant for vector storage, and Agno (phidata prev) for agent orchestration. This application features intelligent query rewriting, document processing, and web search fallback capabilities to provide comprehensive AI-powered responses. ## Features - **Document Processing** - PDF document upload and pro
A list of Python package dependencies including agno, exa, qdrant-client, langchain, and streamlit.
agno exa==0.5.26 qdrant-client==1.12.1 langchain-qdrant==0.2.0 langchain-community==0.3.13 streamlit==1.41.1