Updating vector values
In dynamic environments, data evolves rapidly, and being able to seamlessly integrate new values and metadata into existing vectors keeps your applications relevant and accurate.
In this exercise, you'll practice updating vectors in the 'datacamp-index' Pinecone index with new values. You'll verify these changes were successful by fetching the vector and checking its metadata.
This exercise is part of the course
Vector Databases for Embeddings with Pinecone
Exercise instructions
- Initialize the Pinecone connection with your API key.
- Update the vector with ID
"7"to use the values stored invector. - Fetch the vector to check if the values have been updated.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Initialize the Pinecone client with your API key
pc = Pinecone(api_key="____")
index = pc.Index('datacamp-index')
# Update the values of vector ID 7
____
# Fetch vector ID 7
fetched_vector = ____
print(fetched_vector)