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.
@sumanth_077
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Chat with Your Data: Summarizing PDF Files in App
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Step 4: Chat with your data Finally, once the data has been ingested into the app, chat with your data. Here we are asking it to summarize the pdf file.
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Upload PDF Research Paper Data to Clarifai Vector Database
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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.
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Setting up RAG system with Clarifai and Gemini-1.5 Flash model
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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
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Install Clarifai SDK Llama-index Setup Guide Instructions
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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 Using 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 RAG LLM App Chat PDF Files Gemini Flash
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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):
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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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Implementing PCA Transform and Inverse Transform Methods
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Now define the "transform" method that transforms the input data into a lower-dimensional representation using the calculated eigenvectors. Finally the "inverse_transform" method to reconstructs the reduced data back to the original space using eigenvectors and mean.
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Eigenvectors and Principal Components for Dimensionality Reduction
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Eigen vectors helps in identifying the directions of highest variance in the data, which are then used to construct principal components. These principal components allows you to represent the data in a lower-dimensional while saving as much meaningful information as possible.