Step-5: Add @streamlit components to build a frontend for the search. Let's break it down what all we would need: 1. We need users to be able to input their search query.
2. Cohere's model to convert text to embedding.
3. Finding similar sentences based on Cosine similarity.
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Building AI-Powered Search Frontend with Streamlit Components
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Export Python Code from Playground to IDE
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Step-4: Export the Python code from the Playground and paste it into an IDE.
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Visualizing Embeddings for Semantic Search Understanding
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Step-3: Visualize the embeddings in the playground to understand how semantic search works. Semantically similar sentences would be dots closer to each other and vice-versa.
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Upload Datasets for AI Search in Chohere Playground
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Step-2: Upload the dataset which you want to make searchable either in CSV form or upload it directly via Chohere's Playground UI.
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Building Twitter Thread Search with Cohere Embed
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Let's build a simple search example for finding twitter thread topics related to Y-Combinator. Step-1: Go to the "Embed" Section in Cohere's Playground. Here is the link –
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Building Google-like AI Search Engine with Python
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How to build Google-like AI-powered search using Python in 15 minutes? (A thread)
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AI Tools Hit Scalability Wall: Bugs and Missing Features
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It‘s all fun and games until you build more than tiny parts with it and realize you‘re stuck with 90% because of bugs and missing functionality.
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Statistics for Data Science: Important Concepts and Practical Use Cases Playlist
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4. Sumanth P I have created an entire playlist of important concepts of statistics for Data Science with practical use cases Check this: http://
youtube.com/playlist?list=
PLfAC1GPuK4j_VCcZGof9s7M4x5wWtJR4y
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Khan Academy: Best Channel for Learning Statistics in Data Science
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1. Khan Academy Arguably the best channel to learn Statistics for Data Science Check this: http://
youtube.com/playlist?list=
PL1328115D3D8A2566
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Functional Python Programming Guide Comprehensive Review
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Overall, "Functional Python Programming" is a comprehensive guide to functional programming in Python. I highly recommend it to anyone looking to deepen their understanding of this exciting and powerful language. Grab your copy now