Embedding

Below you will find an overview table of various text embedding APIs, along with example Python code.

Embedding is more a micro optimization, compared to the Flow Design.

It’s recommended to start with the most convenient one and optimize later.

API	Free Tier	Pricing Model	Docs
OpenAI	~$5 credit	~$0.0001/1K tokens	OpenAI Embeddings
Azure OpenAI	$200 credit	Same as OpenAI (~$0.0001/1K tokens)	Azure OpenAI Embeddings
Google Vertex AI	$300 credit	~$0.025 / million chars	Vertex AI Embeddings
AWS Bedrock	No free tier, but AWS credits may apply	~$0.00002/1K tokens (Titan V2)	Amazon Bedrock
Cohere	Limited free tier	~$0.0001/1K tokens	Cohere Embeddings
Hugging Face	~$0.10 free compute monthly	Pay per second of compute	HF Inference API
Jina	1M tokens free	Pay per token after	Jina Embeddings

Example Python Code

1. OpenAI

from openai import OpenAI

client = OpenAI(api_key="YOUR_API_KEY")
response = client.embeddings.create(
    model="text-embedding-ada-002",
    input=text
)
    
# Extract the embedding vector from the response
embedding = response.data[0].embedding
embedding = np.array(embedding, dtype=np.float32)
print(embedding)

2. Azure OpenAI

import openai

openai.api_type = "azure"
openai.api_base = "https://YOUR_RESOURCE_NAME.openai.azure.com"
openai.api_version = "2023-03-15-preview"
openai.api_key = "YOUR_AZURE_API_KEY"

resp = openai.Embedding.create(engine="ada-embedding", input="Hello world")
vec = resp["data"][0]["embedding"]
print(vec)

3. Google Vertex AI

from vertexai.preview.language_models import TextEmbeddingModel
import vertexai

vertexai.init(project="YOUR_GCP_PROJECT_ID", location="us-central1")
model = TextEmbeddingModel.from_pretrained("textembedding-gecko@001")

emb = model.get_embeddings(["Hello world"])
print(emb[0])

4. AWS Bedrock

import boto3, json

client = boto3.client("bedrock-runtime", region_name="us-east-1")
body = {"inputText": "Hello world"}
resp = client.invoke_model(modelId="amazon.titan-embed-text-v2:0", contentType="application/json", body=json.dumps(body))
resp_body = json.loads(resp["body"].read())
vec = resp_body["embedding"]
print(vec)

5. Cohere

import cohere

co = cohere.Client("YOUR_API_KEY")
resp = co.embed(texts=["Hello world"])
vec = resp.embeddings[0]
print(vec)

6. Hugging Face

import requests

API_URL = "https://api-inference.huggingface.co/models/sentence-transformers/all-MiniLM-L6-v2"
HEADERS = {"Authorization": "Bearer YOUR_HF_TOKEN"}

res = requests.post(API_URL, headers=HEADERS, json={"inputs": "Hello world"})
vec = res.json()[0]
print(vec)

7. Jina

import requests

url = "https://api.jina.ai/v2/embed"
headers = {"Authorization": "Bearer YOUR_JINA_TOKEN"}
payload = {"data": ["Hello world"], "model": "jina-embeddings-v3"}
res = requests.post(url, headers=headers, json=payload)
vec = res.json()["data"][0]["embedding"]
print(vec)