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.
Build an autonomous RAG Streamlit app using GPT-4o, PgVector, PDF uploads, and DuckDuckGo web search.
**🎓 FREE Step-by-Step Tutorial** **👉 [Click here to follow our complete step-by-step tutorial](https://www.theunwindai.com/p/build-autonomous-rag-app-using-gpt-4o-and-vector-database) and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.** This Streamlit application implements an Autonomous Retrieval-Augmented Generation (RAG) system using Op
Lists Python package dependencies for a Streamlit AI app with OpenAI, pgvector, and search.
streamlit agno openai psycopg-binary pgvector requests sqlalchemy pypdf duckduckgo-search nest_asyncio
Build a RAG system with real-time step-by-step reasoning using Agno, Gemini 2.5 Flash, and OpenAI embeddings.
### 🎓 FREE Step-by-Step Tutorial **👉 [Click here to follow our complete step-by-step tutorial](https://www.theunwindai.com/p/build-an-agentic-rag-app-with-reasoning) and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.** A sophisticated RAG system that demonstrates an AI agent's step-by-step reasoning process using Agno, Gemini and OpenAI. T
Python package dependencies for a Streamlit app using Agno, LanceDB, OpenAI, and dotenv.
streamlit agno>=2.2.10 lancedb openai python-dotenv
Tutorial and setup guide for a local agentic RAG app using EmbeddingGemma, Llama 3.2, LanceDB, and Streamlit.
## 🔥 Agentic RAG with EmbeddingGemma ### 🎓 FREE Step-by-Step Tutorial **👉 [Click here to follow our complete step-by-step tutorial](https://www.theunwindai.com/p/build-a-local-agentic-rag-app-with-google-embeddinggemma) and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.** This Streamlit app demonstrates an agentic Retrieval-Augmented Gene
A list of Python package dependencies including Streamlit, Agno, LanceDB, Ollama, and PyPDF.
streamlit agno>=2.2.10 lancedb ollama pypdf
LangGraph-powered agentic RAG app using Gemini, Qdrant, and Streamlit to retrieve and answer queries from AI blog posts.
## Overview AI Blog Search is an Agentic RAG application designed to enhance information retrieval from AI-related blog posts. This system leverages LangChain, LangGraph, and Google's Gemini model to fetch, process, and analyze blog content, providing users with accurate and contextually relevant answers. ## LangGraph Workflow  for faithful, retrieval-grounded answers - Reranking of retri
A pip requirements file specifying versioned dependencies for a Streamlit and Pydantic project.
streamlit==1.40.2 contextual-client>=0.1.0 requests>=2.32.0 pydantic==2.9.2
Document Q&A app using hybrid RAG search, Claude, OpenAI embeddings, and Cohere reranking via Streamlit.
A powerful document Q&A application that leverages Hybrid Search (RAG) and Claude's advanced language capabilities to provide comprehensive answers. Built with RAGLite for robust document processing and retrieval, and Streamlit for an intuitive chat interface, this system seamlessly combines document-specific knowledge with Claude's general intelligence to deliver accurate and contextual responses
A pip requirements file listing Python packages for an AI/RAG application stack.
raglite==0.2.1 pydantic==2.10.1 sqlalchemy>=2.0.0 psycopg2-binary>=2.9.9 openai>=1.0.0 cohere>=4.37 pypdf>=3.0.0 python-dotenv>=1.0.0 rerankers==0.6.0 spacy>=3.7.0 streamlit anthropic
Agentic RAG system using Cohere, Qdrant, LangChain, and LangGraph with web search fallback.
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 chunk
Lists Python package requirements for a LangChain, Cohere, Qdrant, and Streamlit AI application.
langchain==0.3.12 langchain-community==0.3.12 langchain-core==0.3.25 langchain-cohere==0.3.2 langchain-qdrant==0.2.0 cohere==5.11.4 qdrant-client==1.12.1 duckduckgo-search==6.4.1 streamlit==1.40.2 tenacity==9.0.0 typing-extensions==4.12.2 pydantic==2.9.2 pydantic-core==2.23.4 langgraph==0.2.53
Build a production-ready RAG pipeline using Claude 3.5 Sonnet and Ragie.ai with a Streamlit UI in under 50 lines.
## 🖇️ RAG-as-a-Service with Claude 3.5 Sonnet Build and deploy a production-ready Retrieval-Augmented Generation (RAG) service using Claude 3.5 Sonnet and Ragie.ai. This implementation allows you to create a document querying system with a user-friendly Streamlit interface in less than 50 lines of Python code. ### Features - Production-ready RAG pipeline - Integration with Claude 3.5 Sonnet for
Lists Python package dependencies: streamlit, anthropic, and requests.
streamlit anthropic requests
Build a local RAG agent using Deepseek, Qdrant, and Snowflake embeddings with PDF ingestion and web search.
### 🎓 FREE Step-by-Step Tutorial **👉 [Click here to follow our complete step-by-step tutorial](https://www.theunwindai.com/p/build-a-local-rag-reasoning-agent-with-deepseek-r1) and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.** A powerful reasoning agent that combines local Deepseek models with RAG capabilities. Built using Deepseek (via
A requirements list for a Python project using Agno, Exa, Qdrant, LangChain, Streamlit, and Ollama.
agno exa==0.5.26 qdrant-client==1.12.1 langchain-qdrant==0.2.0 langchain-community==0.3.13 streamlit==1.41.1 ollama
RAG-based system for querying pharmaceutical research papers using LangChain, ChromaDB, and Google Gemini.
## Overview PharmaQuery is an advanced Pharmaceutical Insight Retrieval System designed to help users gain meaningful insights from research papers and documents in the pharmaceutical domain. ## Demo https://github.com/user-attachments/assets/c12ee305-86fe-4f71-9219-57c7f438f291 ## Features - **Natural Language Querying**: Ask complex questions about the pharmaceutical industry and get concise,
A list of Python package dependencies for a Streamlit LangChain Google Gemini RAG application.
streamlit langchain-google-genai langchain-chroma langchain-community langchain-core chromadb sentence-transformers PyPDF2 python-dotenv
Build a local RAG system using Qwen3/Gemma3 via Ollama with PDF ingestion, Qdrant vector search, and web search fallback.
This RAG Application demonstrates how to build a powerful Retrieval-Augmented Generation (RAG) system using locally running Qwen 3 and Gemma 3 models via Ollama. It combines document processing, vector search, and web search capabilities to provide accurate, context-aware responses to user queries. Built with Agno v2.0. ## Features - **🧠 Multiple Local LLM Options**: - Qwen3 (1.7b, 8b) - Alib
Lists Python package dependencies for an AI project using Agno, Qdrant, LangChain, Streamlit, and Ollama.
agno>=2.2.10 pypdf exa qdrant-client langchain-qdrant langchain-community streamlit ollama
Streamlit app using MCP and Playwright to control a browser with natural language commands and LLMs.
https://github.com/user-attachments/assets/a01e09fa-131b-479a-8df3-2d1a61fd80f3 A Streamlit application that allows you to browse and interact with websites using natural language commands through the Model Context Protocol (MCP) and [MCP-Agent](https://github.com/lastmile-ai/mcp-agent) with Playwright integration. ## Features - **Natural Language Interface**: Control a browser with simple Engl
A requirements.txt listing Streamlit, MCP Agent, OpenAI, and asyncio package dependencies.
streamlit>=1.28.0 mcp-agent>=0.0.14 openai>=1.0.0 asyncio>=3.4.3
A Streamlit app routing queries to specialized AI agents—code reviewer, security auditor, researcher, BIM engineer—each with tailored MCP tools.
A Streamlit app that demonstrates the **multi-agent + MCP** pattern: specialized AI agents that each connect to different MCP servers to handle domain-specific tasks. Instead of one agent with all tools, The router sends your request to a **specialist** — a code reviewer, security auditor, researcher, or BIM engineer — each with access to only the MCP tools they need. ## Features - **4 Speciali
A pip requirements file specifying minimum versions for Streamlit, Anthropic, MCP, and Pydantic.
streamlit>=1.28.0 anthropic>=0.40.0 mcp>=0.1.0 pydantic>=2.0.0
A terminal-based AI agent for managing Notion pages via natural language using the Model Context Protocol.
### 🎓 FREE Step-by-Step Tutorial **👉 [Click here to follow our complete step-by-step tutorial](https://www.theunwindai.com/p/build-a-terminal-based-notion-agent-with-mcp) and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.** A terminal-based Notion Agent for interacting with your Notion pages using natural language through the Notion MCP (M
A list of Python package dependencies including agno, dotenv, mcp, openai, and sqlalchemy.
agno>=2.2.10 python-dotenv mcp openai sqlalchemy
Integrates GitHub, Perplexity, Calendar, and Gmail via MCP servers into a natural language AI productivity assistant.
The Multi-MCP Intelligent Assistant is a powerful productivity tool that integrates multiple Model Context Protocol (MCP) servers to provide seamless access to GitHub, Perplexity, Calendar, and Gmail services through natural language interactions. This advanced AI assistant is powered by Agno's AI Agent framework and designed to be a productivity multiplier across your digital workspace. ## Featu
A minimal requirements file listing agno, openai, mcp, and python-dotenv dependencies.
agno>=2.2.10 openai mcp python-dotenv