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Showing content from https://python.langchain.com/docs/integrations/text_embedding/jina/ below:

Jina | 🦜️🔗 LangChain

You can check the list of available models from here.

Install requirements

pip install -U langchain-community

Import libraries

import requests
from langchain_community.embeddings import JinaEmbeddings
from numpy import dot
from numpy.linalg import norm
from PIL import Image

text_embeddings = JinaEmbeddings(
    jina_api_key="jina_*", model_name="jina-embeddings-v2-base-en"
)

text = "This is a test document."

query_result = text_embeddings.embed_query(text)

doc_result = text_embeddings.embed_documents([text])

multimodal_embeddings = JinaEmbeddings(jina_api_key="jina_*", model_name="jina-clip-v1")

image = "https://avatars.githubusercontent.com/u/126733545?v=4"

description = "Logo of a parrot and a chain on green background"

im = Image.open(requests.get(image, stream=True).raw)
print("Image:")
display(im)

image_result = multimodal_embeddings.embed_images([image])

description_result = multimodal_embeddings.embed_documents([description])

print(description_result)

cosine_similarity = dot(image_result[0], description_result[0]) / (
    norm(image_result[0]) * norm(description_result[0])
)

• Embedding model conceptual guide
• Embedding model how-to guides

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