jina-embeddings-v2-base-en

!pip install pinecone

from pinecone import Pinecone, ServerlessSpec

pc = Pinecone(api_key="API_KEY")

# Create Index
index_name = "jina-embeddings-v2-base-en"

if not pc.has_index(index_name):
    pc.create_index(
        name=index_name,
        dimension=768,
        metric="cosine",
        spec=ServerlessSpec(
            cloud='aws',
            region='us-east-1'
        )
    )

index = pc.Index(index_name)

# 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."},
]

import requests
url = 'https://api.jina.ai/v1/embeddings'


def get_embeddings(texts):
  # returns embeddings given list of texts
  headers = {
      'Content-Type': 'application/json',
      'Authorization': f'Bearer {JINA_API_KEY}'
  }
  data = {
      'input': texts,
      'model': 'jina-embeddings-v2-base-en'
  }
  response = requests.post(url, headers=headers, json=data)
  return response.json()


embeddings = get_embeddings([d["text"] for d in data])

embeddings = [e["embedding"] for e in embeddings["data"]]

vectors = []
for d, e in zip(data, embeddings):
    vectors.append({
        "id": d['id'],
        "values": e,
        "metadata": {'text': d['text']}
    })

index.upsert(
    vectors=vectors,
    namespace="ns1"
)

query = "Tell me about the tech company known as Apple"

x = get_embeddings([query])["data"][0]["embedding"]

results = index.query(
    namespace="ns1",
    vector=x,
    top_k=3,
    include_values=False,
    include_metadata=True
)

print(results)