This project demonstrates a Retrieval-Augmented Generation (RAG) architecture using open-source t...This project demonstrates a Retrieval-Augmented Generation (RAG) architecture using open-source t...
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yash varun
• 21h
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.
PythonBackend DevelopmentFrontend DevelopmentFull Stack DevelopmentJavaScriptOpenAI
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