File size: 1,051 Bytes
7f5ef51 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 |
from pinecone import Pinecone
pc = Pinecone("pcsk_3MGbHp_26EnMmQQm72aznGSw4vP3WbWLfbeHjeFbNXWWS8pG5kdwSi7aVmGcL3GmH4JokU")
# Embed data
data = [
{"id": "vec1", "text": "Apple is a popular fruit known for its sweetness and crisp texture."},
{"id": "vec2", "text": "The tech company Apple is known for its innovative products like the iPhone."},
{"id": "vec3", "text": "Many people enjoy eating apples as a healthy snack."},
{"id": "vec4", "text": "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces."},
{"id": "vec5", "text": "An apple a day keeps the doctor away, as the saying goes."},
]
embeddings = pc.inference.embed(
model="llama-text-embed-v2",
inputs=[d['text'] for d in data],
parameters={
"input_type": "passage"
}
)
vectors = []
for d, e in zip(data, embeddings):
vectors.append({
"id": d['id'],
"values": e['values'],
"metadata": {'text': d['text']}
})
index.upsert(
vectors=vectors,
namespace="ns1"
)
|