Step 3: Upload the data, Here we are uploading the pdf file of a research paper. Once you upload the data, it will embed and index the data into your Clarifai app. Clarifai Apps acts as your vector database.
@sumanth_077
-
Setting Up RAG System with Clarifai and Gemini-1.5 Flash
By
–
Step 2: Next, import the RAG class and set up the RAG system by passing your Clarifai user ID and the Gemini-1.5 Flash model URL from the Clarifai community. You can find it here: http://
clarifai.com/gcp/generate/m
odels/gemini-1_5-flash
… Once you sign up, you can find your user ID here: -
Install Clarifai SDK, Llama-index, and Configure Personal Access Token
By
–
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
… -
Build RAG System in Four Lines with Clarifai SDK
By
–
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:
-
Build RAG LLM App Chat PDF Files Gemini Flash Python
By
–
Build a LLM app with RAG to chat with PDF files using Gemini Flash in just 4 lines of Python code (step-by-step instructions):
-
Daily Content Sharing on Python Data Science and Machine Learning
By
–
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.
-
_predict_single Method for K-Nearest Neighbours Classification
By
–
Finally a '_predict_single' method as mentioned it predicts the class label for a single data point This is done by: 1. Calculating the distance between one point to all training examples 2. Selecting the k-nearest Neighbours, & returning the most common class label among them
-
Predict Method for Class Labels Using Single Point Prediction
By
–
A 'predict' method that predicts the class labels for a set of data points. It calls '_predict_single' method that actually predicts the class label for a single data point.
-
KNN fit Method and Euclidean Distance Calculation
By
–
Next define the "fit" method that takes the training data(X) and it's corresponding labels(y) and stores them. This is the reason why the computation cost is high as it stores all the training data! And also a method to calculates the euclidean distance between two data points.
-
KNNClassifier Class Initialization: Choosing the Optimal k Value
By
–
Define the KNNClassifier class and initialize the number of Neighbours (k) to be considered in __init__ method Defining the K value is critical in KNN! A small value of k means noise will have a higher influence on the result and a large value makes it computationally expensive