This is just place i visited, sick place🔥

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Cover image for this is just to complete my 4 submissions. Because i signed ...

this is just to complete my 4 submissions. Because i signed NDA docs😭😭😭

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This project demonstrates a Retrieval-Augmented Generation (RAG) architecture using open-source tools. It solves the "knowledge cutoff" problem of LLMs by giving them access to live tools. How it works: Intent Classification: The user's input is first processed by a local Llama 3 instance, which acts as a semantic router. It analyzes the prompt to determine if external data is required. Tool Execution: If a search intent is detected, the Python backend executes a query against the SerpApi (Google Shopping) endpoint. Data Structuring: The raw JSON response from the API is parsed, cleaned, and structured into standardized product objects. UI Rendering: The structured data is dynamically rendered into interactive product cards using Streamlit's component system, allowing users to view images and click direct purchase links without leaving the chat interface.

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Retrieval- Augmented Generation (RAG) web app that synthesizes real time data from PUBMed.com (http://PUBMed.com) and ClinicalTrials.gov (http://ClinicalTrials.gov), a custom React frontend and Node.js backend with MongoDB backend multi turn memory to maintain conversational context and prevent AI hallucinations

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yash varun

A developer that love to build!