Step 2: Next, import the RAG class and set up the RAG system by passing your Clarifai user ID and the GPT-4o(omni) model URL from the Clarifai community. You can find it here: https://
clarifai.com/openai/chat-co
mpletion/models/gpt-4o
… 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 Model
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Install Clarifai SDK Llama-index Setup Guide
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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 to Chat with PDFs Using GPT-4 Omni
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Build a LLM app with RAG to chat with PDF files using GPT-4 o (omni) in just 4 lines of Python code (step-by-step instructions):
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Daily Content Sharing on Python, Data Science, and Machine Learning
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That's a wrap! If you are interested in any of these below 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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Predict Method Implementation with Binary Classification Threshold
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Finally the predict method: It uses the trained model to make predictions based on input features. This method applies a threshold of 0.5 to make binary predictions, returning 1 for positive class predictions and 0 for negative class predictions.
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Implementing the Fit Method for Model Training with Gradient Descent
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Define the "fit" method that does the training of model The method takes input data "X" and the corresponding target values "y" It also updates the weights and bias using gradient descent.
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Computing Cross-Entropy Loss for Classification Models
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Compute Cost Function Next define the cost function, which is the cross-entropy loss, used to measure the error between predicted probabilities and actual labels.
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Sigmoid Activation Function: Probability Computation Between 0 and 1
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Define Sigmoid Activation Function: This is used to compute the probability of the target class. Sigmoid function takes a linear combination of input features and returns values between 0 and 1, which represent the probability that a given sample belongs to the positive class.
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Define LogisticRegression Class with Five Essential Methods
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Define LogisticRegression class to encapsulate the functionality of Logistic Regression The class has 5 methods: __init__, sigmoid, compute_cost, fit and predict. First let's Initialize learning rate, epochs, weights and bias attributes using the __init__ method.