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GitDoc is your ultimate GitHub Documentation Explorer! It's your trusty sidekick for navigating through the vast world of open-source projects, making code exploration and documentation retrieval a breeze. 🚀
img2card is a unique Telegram Bot designed to simplify your contact management. It takes facade pictures or contact card pictures as input and generates vCard files
The "lcel-tutorial" repo is designed for mastering LangChain Expression Language (LCEL), offering exercises to build stateful, multi-actor LLM applications. It's a hands-on guide to leveraging LCEL for complex workflows and agent-like behaviors. Perfect for enthusiasts eager to explore LLM's potential.
Discover LangSmith: Your gateway to crafting production-grade LLM (Large Language Model) applications with ease. Dive into a world of innovation and efficiency as LangSmith empowers you to create sophisticated language-driven solutions effortlessly.
Trainer AI is an LLM assistant agent with the goal of helping you workout more efficiently, and spend less time preparing workout sets, and analyzing data. You talk to it like a personal coach, and it records your efforts, and lays plans for you to reach your goals.
LangChain agent that given a name, searches in google to find Linkedin and Twitter profiles, scrape the internet for information about a name you provide and generate a couple of personalized ice breakers to kick off a conversation with the person.
Website-to-MCQs is an application built in Python that utilizes generative AI, Langchain, embedding techniques, and ChatGPT to automatically generate multiple-choice questions (MCQs) from website content.
This AI Smart Speaker uses speech recognition and text-to-speech to enable voice-driven conversations and vision capabilities with OpenAI and Agents. The user speaks a prompt into the microphone, and the program sends the prompt to OpenAI to generate a response. The response is then converted to an audio file and played back to the user.
"Chat with Databases using RAG" is a cutting-edge project that seamlessly integrates natural language inputs with database interactions. By leveraging advanced techniques like RAG and few-shot learning, it generates SQL queries from plain text and retrieves human-like responses from the database, revolutionizing the way we interact with data.