From the course: Scalable Data Storage and Processing for AI Workloads
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Semantic search with Pinecone
From the course: Scalable Data Storage and Processing for AI Workloads
Semantic search with Pinecone
- In this demo, we'll see a simple example of how you can generate embeddings from your text data. Store that in a vector database and then ask questions of your data. You'll see that the Vector database matches the embeddings of your questions, the embeddings of the responses to give you fairly accurate responses. Now, the vector database that we work within this demo is the free trial of the Pinecone database. Head over to app.pinecone.io. Pinecone is a very popular vector database designed for storing, indexing and querying, high dimensional vector embeddings. Very commonly used in AI and ML applications. All you need to do here is create an account and set up a vector search index. I'm going to log in with my Google account. So I continue with Google and just use my logged in user account to create an account with Pinecone. Now you can customize your Pinecone setup, but really, you don't need to do anything. I just specify the name of my company. We'll be using the Python…
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