Using Pinecone Inference API

pc.create_index_for_model(
    name="docs",
    cloud="aws",
    region="us-east-1",
    embed={
        "model": "multilingual-e5-large",
        "field_map": {"text": "chunk_text"},
    },
)

r = pc.inference.embed(
    model="multilingual-e5-large",
    inputs=["document text"],
    parameters={"input_type": "passage", "truncate": "END"},
)

texts = ["doc 1", "doc 2", "doc 3"]
r = pc.inference.embed(model="multilingual-e5-large",
    inputs=texts, parameters={"input_type": "passage"})
vectors = [e["values"] for e in r.data]

index.upsert_records(
    namespace="docs",
    records=[
        {"id": "d1", "chunk_text": "Vector DB intro", "source": "wiki"},
        {"id": "d2", "chunk_text": "Pinecone basics", "source": "blog"},
    ],
)
# Pinecone embeds chunk_text automatically using the index's model