Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat
To manage Pinecone data well, you need a storage system that can handle different types of data easily, like MongoDB. Let's see how MongoDB's way of handling data can help store Pinecone data better: 1. Handling Different Types of Data: MongoDB is good at handling different
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Generative AI | Artificial Intelligence | LangChain | MongoDB Vector embeddings are like compact representations of data, consisting of arrays of floating-point numbers. While they may seem small individually, when you have to manage thousands of them, storage and comparison become significant concerns. Many databases aren't optimized for handling such vectors
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It can deal with high-dimensional vector data at a higher scale, easy integration, and faster query results. Pinecone provides a reliable, and faster, option for searching at a higher scale. * Pinecone Serverless is a managed service offering of Pinecone that allows users to deploy and run Pinecone's vector