I am learning about different concepts and architectures used in the LLM/AI space and one of them is Retrieval-Augmented Generation. As always I prefer learning concepts by tinkering with them and here is my first attempt at learning about RAG and Vector Databases.
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Create a free pod index in Pinecone using Python
Pinecone documentation is quite good, but when I wanted to create a free pod index in Pinecone using Python I didn’t know what parameters I should supply.
Specifically, I couldn’t understand what values would be the values for environment and pod_type
After a bit of digging (looking at the WebUI) here is how to do it
from pinecone import Pinecone, PodSpec
pc = Pinecone(api_key='<<PINECONE_API_KEY>>')
pc.create_index(
name="example-index",
dimension=1536,
metric="cosine",
spec=PodSpec(
environment='gcp-starter',
pod_type='s1.x1'
)
)