Step 2: Next, import the RAG class and set up the RAG system by passing your Clarifai user ID and the GPT-4o mini model URL from the Clarifai community. You can find it here: https://
clarifai.com/openai/chat-co
mpletion/models/gpt-4o-mini
… Once you sign up, you can find your user ID here: https://
clarifai.com/settings
@sumanth_077
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Setting up RAG system with Clarifai and GPT-4o mini model
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Installing Clarifai SDK, Llama-index, and Setting Up PAT
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Step 1: Install the Clarifai Python SDK, Llama-index, and set your Clarifai Personal Access Token as an environment variable. Sign up for the platform, and you can access the PAT here: https://
clarifai.com/settings/secur
ity
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Build RAG System in 4 Lines with Clarifai SDK
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You can build a RAG system with just 4 lines of code using @clarifai Python SDK. Below is the entire code. Let's look at it step by step:
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Build LLM RAG App Chat PDF Files GPT-4 Mini
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Build a LLM app with RAG to chat with PDF files using GPT-4 o mini in just 4 lines of Python code:
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Daily Content on Python, Data Science, Machine Learning and MLOps
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That's a wrap! If you are interested in any of these topics: – Python – Data Science – Machine Learning – Data Analysis – LLMs – MLOps Find me → @Sumanth_077 I'm sharing daily content over here.
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Capstone Project: Build a Data Science Project from Scratch
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6. Capstone Project: Build a Data science project from scratch: After completing all the above courses take Harvard’s data science capstone project. Assess your skills in data visualization, statistics, data wrangling and machine learning.
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Machine Learning Fundamentals: Overfitting, Modeling, and Recommendations
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5. Machine Learning This course will teach you the basics of machine learning, techniques to mitigate overfitting, supervised and unsupervised modelling approaches, and recommendation systems.
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Data Pre-Processing: Preparing Data for Machine Learning Models
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3. Data Pre-Processing: This will teach you to prepare data and convert it into a format that is easily digestible by machine learning models.
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Neural Network Training Method: Forward Pass, Backward Pass, Weight Updates
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Now a method to train the neural network using both the forward and backward passes. The function will run for specified no of epochs, calculating: 1. The Forward Pass
2. Backward Pass
3. Updating the Weights -
Update Weights Learning Rate Hyperparameter Optimization Neural Networks
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Now Update the Weights: Here learning rate is the hyper parameter! A low learning rate can cause the model getting caught in local optima, while the high learning rate can cause the model to overshoot the general solution W1 += learning_rate * d_W1
b1 += learning_rate * d_b1