⚡️Framework for fast persistent storage of multiple document embeddings and metadata into Pinecone for source-traceable, production-level RAG.
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Updated
Dec 23, 2024 - Python
⚡️Framework for fast persistent storage of multiple document embeddings and metadata into Pinecone for source-traceable, production-level RAG.
These #LangChain-powered apps include a Research Assistant that generates reports using web scraping and GPT-4o-mini, and Chat With Video, a #Streamlit app that #transcribes videos and enables content-based Q&A via #embeddings.
These #LangChain-powered apps include a Research Assistant that generates reports using web scraping and GPT-4o-mini, and Chat With Video, a #Streamlit app that #transcribes videos and enables content-based Q&A via #embeddings.
Feeling lonely again? Don't worry — talk to YouTube videos this time 💔🩹
E-commerce Customer Service Chatbot: A GenAI-powered chatbot that answers user queries, provides recommendations, tracks orders, and more.
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Experimenting with Pinecone as vector data continues to take center stage in AI-native systems. The purpose of this project is to explore the core capabilities, benchmark performance across different embedding models, and better understand what is possible with vector search in production environments.
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This project is a conversational chatbot integrated with the Pinecone vector database.
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