{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/82bbc6b267f24f98a32be7ea332bf759\" frameborder=\"0\" width=\"1920\" height=\"1440\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1440,"width":1920,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1440,"thumbnail_width":1920,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/82bbc6b267f24f98a32be7ea332bf759-0bcf8b981809aa90.gif","duration":393.582,"title":"Sentence Embeddings Similarity Matching for Leads 🚀","description":"Hi, I am Prasant Gupta, and I built a simple ML based similarity system for KBuilder. I convert incoming prompts and existing inputs into vector embeddings using a pre trained Sentence Transformer model, then compute cosine similarity to find the best match. This enables semantic matching even when wording differs, like cost and pricing. I walked through the code flow, from loading the model and generating embeddings to selecting the highest scoring input and returning matches with similarity scores. I did not request any specific action from viewers."}