Let's walk through how to develop a multiagent workflow in LangGraph using the DeepSeek R1 model. We're going to develop RAG and tabular data agents.
This tutorial explains how to perform Retrieval Augmented Generation (RAG) with the Python LangGraph framework to enhance the default knowledge of large language models.
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Learn to use the Python LangGraph framework to develop your own multi-agent chatbots. Study how to use ReAct agents in LangGraph's multi-agent systems.
Learn to develop a retrieval augmented generation (RAG) chatbot for YouTube video question-answering using Alibaba Qwen 2.5 Model from Hugging Face.
This tutorial teaches you how to predict future time series data using a transformer model from the Hugging Face library in Python. To illustrate this, we will classify stock market data.
We created a suite of 6 VBA cheat sheets with over 200 tips showing you everything you need to know to start making power Excel applications. Take a look!
This article presents a comparison of Anthropic Claude 3.5 Sonnet LLM with OpenAI GPT 4o for zero-shot text classification and text summarization.
Learn how to develop a chatbot using open-source Meta Llama 3.1 model in LangChain. We'll also show you how to import this open-source model from Hugging Face in LangChain.
We'll teach you the basics of Python LangChain agents, including how to use built-in LangChain agents to access third party tools, and how to create custom agents with memory.
Learn to handle missing data in Pandas DataFrames using the Python feature-engine library. We'll show you how to handle missing numerical and categorical data.
This tutorial explains how to fine-tune Meta's Llama-2 model for question answering. We'll walk you through how to use Python to fine-tune the Llama-2 model on a custom dataset.
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