Tech With Tim·2y5 Python AI Project Ideas & HOW To Build Them
Discover five Python projects that guide you from easy to complex AI implementations. The projects include sentiment analysis, image classification, voice assistants, recommendation systems, and AI agents. Each project comes with recommended libraries and code samples, making them excellent for users looking to enhance their Python and AI skills.
Community Picks·2yBuilding an AI-powered quiz application with Next.js and OpenAI
Learn how to build an AI-powered quiz application using Next.js and OpenAI. The tutorial covers creating a dynamic quiz interface, leveraging OpenAI to generate questions, managing user score, and best practices for integrating AI features. Additionally, it introduces Latitude AI, a platform for developing AI applications, and guides you through setting up the project, from creating a new Next.js app to deploying AI-powered features.
Community Picks·2yraznem/parsera: Lightweight library for scraping web-sites with LLMs
Parsera is a lightweight Python library designed for scraping websites using large language models (LLMs). It is easy to set up with minimal token use, boosting speed and reducing costs. Users can configure it to use models from OpenAI or Azure, and it includes asynchronous support. The library can extract specified elements from web pages and return the results in JSON format.
freeCodeCamp·2yHow to Use GPT to Analyze Large Datasets
Leveraging GPT and related tools can significantly streamline the process of analyzing large datasets and summarizing content quickly. The post describes how to convert a 90-minute video conference using OpenAI Whisper into a transcript, which is then summarized through ChatPDF. It further elaborates on using GPT for complex business analytics, including preparing datasets and employing LlamaIndex to extract insights, such as identifying geographic regions with the highest household wealth. However, users must understand the context of their data and create specific prompts to ensure reliable outcomes.
SingleStore·2yHow to Create a Full-Stack GenAI App Using SingleStore, OpenAI and Next.js
Learn how to build a full-stack GenAI app using SingleStore, OpenAI, and Next.js. This step-by-step tutorial guides you in creating a micro gen AI app that enables chat with gpt-4o, retrieves random products, and renders them in custom React components. Key features include a text-to-SQL chat experience, efficient parallel query execution, and hybrid search capabilities.
Bytes by ui.dev·2yAI in JavaScript-Land
Recent releases from OpenAI, Vercel, and Supabase are making waves in AI development for JavaScript. OpenAI introduced Structured Outputs for better JSON schema handling, improving its accuracy to 100%. Vercel's AI SDK 3.3 includes features like multi-modal file attachments and streamed object generation. Supabase launched postgres.new, a tool connecting AI to Postgres databases and generating SQL queries. Convex added a comprehensive auth library to simplify user authentication. Other highlights include new fonts created with Rust, and innovative tools and studies in the JavaScript ecosystem.
Vaadin·2yBuilding an OpenAI-powered chatbot in Java
Learn how to build a chatbot using OpenAI's Chat Completions API integrated into a Vaadin Flow chat application. The post walks you through creating a simple chat UI, setting up a Java API to interact with OpenAI, and enabling both synchronous and asynchronous chat functionalities. It also covers using WebSockets for real-time updates and provides tips for customizing Vaadin components. The complete source code is available on GitHub.
Community Picks·2yOpenai Assistant API Starter Template
Create an OpenAI Assistant via the dashboard by giving it a name and instructions, then obtain the Assistant ID. Generate an API key and save it securely. In Appsmith, set up a new Authenticated API Datasource with the given settings. Create and configure the necessary APIs to build a chat-style experience, including creating a thread, adding messages, running the thread, and reading the assistant's replies. Finally, design the UI with input, text, and button widgets to facilitate interaction, and deploy the app for testing.
Towards Data Science·2yHow to Implement a GenAI Agent using Autogen or LangGraph
GenAI agents can automate parts of business processes that involve tasks like text summarization, question answering, and code generation. This post demonstrates implementing a GenAI agent using two frameworks: Autogen, which treats workflows as conversations between agents, and LangGraph, which represents workflows as graphs. Step-by-step guides include setting up an agent framework to query weather information using APIs, handling location extraction, geocoding, and obtaining the final answer from the NWS API. Both frameworks are showcased with configurations for various AI models.
GoPenAI·2yBuilding a Database-Driven Chatbot with LangChain and OpenAI: A Practical Approach (Part 1, Warm-up)
The post provides a step-by-step guide to building a database-driven chatbot using LangChain and OpenAI. It covers setting up the project, initializing necessary APIs, and creating a basic LangChain application. Key aspects include generating SQL queries from natural language inputs, connecting to an SQLite database, and parsing query outputs for execution. By the end, readers will have a basic chatbot capable of aiding airline ground staff in tracking passenger baggage, with a promise of more advanced features in future sections.
Towards Data Science·2yBuilding a RAG Pipeline with MongoDB: Vector Search for Personalized Picks
Explore building a personalized movie recommendation system using a Retrieval-Augmented Generation (RAG) pipeline and MongoDB’s vector search capabilities. By integrating large language models (LLMs) and structured data retrieval, this project showcases how to handle user queries and generate accurate recommendations. Steps covered include setting up the environment, data modeling with Pydantic, embedding generation via OpenAI's API, data ingestion into MongoDB, and performing vector search operations.
GoPenAI·2yBuilding a Database-Driven Chatbot with LangChain and OpenAI: A Practical Approach (Part 2, Chaining)
This post is the second part of a series on building a database-driven chatbot using LangChain and OpenAI. It details how to use chaining capabilities in LangChain to link outputs and create a seamless response generation process. By combining multiple chains such as query generation, query execution, and answer generation into one unified chain, the workflow becomes more efficient. The final chain can be invoked with sample input to generate a natural language response from a database query.
