Pinecone Howto
Pinecone is a fully managed vector database that enables fast and scalable similarity search for AI applications.
View MoreHow to Use Pinecone
Sign up for a Pinecone account: Go to the Pinecone website and create an account to get started. You'll receive an API key that you'll need for authentication.
Install the Pinecone client: Install the Pinecone client library for your preferred programming language (e.g. Python) using pip: pip install pinecone-client
Initialize Pinecone client: Import and initialize the Pinecone client in your code using your API key: from pinecone import Pinecone; pc = Pinecone(api_key='YOUR_API_KEY')
Create an index: Create a new serverless index specifying the name, dimension of your vectors, and cloud/region: pc.create_index(name='my-index', dimension=1536, spec=ServerlessSpec(cloud='aws', region='us-east-1'))
Connect to your index: Connect to your newly created index: index = pc.Index('my-index')
Upsert vectors: Insert or update vectors in your index: index.upsert(vectors=[{'id': 'vec1', 'values': [0.1, 0.2, ...], 'metadata': {'key': 'value'}}])
Query the index: Perform vector similarity search on your index: results = index.query(vector=[0.1, 0.2, ...], top_k=10)
Process results: Process and use the query results in your application as needed
Pinecone FAQs
Pinecone is a fully managed vector database designed for machine learning applications. It provides vector search capabilities to enable similarity search, personalization, ranking, and other AI-powered features.
Pinecone Monthly Traffic Trends
Pinecone experienced a 5.2% increase in visits to 507K. The recent integration of AI inferencing and enhancements to its AI knowledge platform likely contributed to this growth by improving the performance and accuracy of AI-powered solutions.
View history traffic
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