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 1.0% decline in traffic, with 465.9K visits in the month. Despite recent updates, including the integration of AI inferencing and the launch of a serverless vector database, the slight decline suggests that these features may not have yet significantly impacted user engagement or that market competition remains strong.
View history traffic
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