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"
)