Get embeddings from Bedrock
Bedrock supports embedding text and images through Amazon Titan and Cohere models. Obiguard provides a standardized interface for embedding multiple modalities.
from obiguard import Obiguard
client = Obiguard(
obiguard_api_key="sk-obg***", # Your Obiguard API key
virtual_key="VIRTUAL_KEY",
)
embeddings = client.embeddings.create(
model="amazon.titan-embed-text-v2:0",
input="Hello this is a test",
# normalize=False # if you would like to disable normalization
# dimensions=1024, # embedding dimensions
# encoding_format="float", # embedding format
)
from obiguard import Obiguard
client = Obiguard(
obiguard_api_key="sk-obg***", # Your Obiguard API key
virtual_key="VIRTUAL_KEY",
)
embeddings = client.embeddings.create(
model="amazon.titan-embed-text-v2:0",
input="Hello this is a test",
# normalize=False # if you would like to disable normalization
# dimensions=1024, # embedding dimensions
# encoding_format="float", # embedding format
)
from openai import OpenAI
from obiguard import OBIGUARD_GATEWAY_URL, createHeaders
client = OpenAI(
api_key='NOT_REQUIRED',
base_url=OBIGUARD_GATEWAY_URL,
default_headers=createHeaders(
provider="openai",
obiguard_api_key="sk-obg******", # Your Obiguard API key
)
)
embeddings = client.embeddings.create(
model="amazon.titan-embed-text-v2:0",
input="Hello this is a test",
# normalize=False # if you would like to disable normalization
# dimensions=1024, # embedding dimensions
# encoding_format="float", # embedding format
)
curl --location 'https://gateway.obiguard.ai/v1/embeddings' \
--header 'Content-Type: application/json' \
--header 'x-obiguard-api-key: $OBIGUARD_API_KEY' \
--data-raw '{
"model": "amazon.titan-embed-text-v2:0",
"input": "Hello this is a test",
"normalize": false, // if you would like to disable normalization
"dimensions": 1024, // embedding dimensions
"encoding_format": "float" // embedding format
}'
from obiguard import Obiguard
client = Obiguard(
obiguard_api_key="sk-obg***", # Your Obiguard API key
virtual_key="VIRTUAL_KEY",
)
embeddings = client.embeddings.create(
model="amazon.titan-embed-image-v1",
dimensions=256,
input=[
{
"text": "this is the caption of the image",
"image": {
"base64": "UklGRkacAABXRUJQVlA4IDqcAACQggKdASqpAn8B....."
}
}
]
)
from obiguard import Obiguard
client = Obiguard(
obiguard_api_key="sk-obg***", # Your Obiguard API key
virtual_key="VIRTUAL_KEY",
)
embeddings = client.embeddings.create(
model="amazon.titan-embed-image-v1",
dimensions=256,
input=[
{
"text": "this is the caption of the image",
"image": {
"base64": "UklGRkacAABXRUJQVlA4IDqcAACQggKdASqpAn8B....."
}
}
]
)
from openai import OpenAI
from obiguard import OBIGUARD_GATEWAY_URL, createHeaders
client = OpenAI(
api_key='NOT_REQUIRED',
base_url=OBIGUARD_GATEWAY_URL,
default_headers=createHeaders(
provider="openai",
obiguard_api_key="sk-obg******", # Your Obiguard API key
)
)
embeddings = client.embeddings.create(
model="amazon.titan-embed-image-v1",
dimensions=256,
input=[
{
"text": "this is the caption of the image",
"image": {
"base64": "UklGRkacAABXRUJQVlA4IDqcAACQggKdASqpAn8B....."
}
}
]
)
curl --location 'https://gateway.obiguard.ai/v1/embeddings' \
--header 'Content-Type: application/json' \
--header 'x-obiguard-api-key: $OBIGUARD_API_KEY' \
--data-raw '{
"model": "amazon.titan-embed-image-v1",
"dimensions": 256,
"input": [
{
"text": "this is the caption of the image",
"image": {
"base64": "UklGRkacAABXRUJQVlA4IDqcAACQggKdASqpAn8B....."
}
}
]
}'
from obiguard import Obiguard
client = Obiguard(
obiguard_api_key="sk-obg***", # Your Obiguard API key
virtual_key="VIRTUAL_KEY",
)
embeddings = client.embeddings.create(
model="cohere.embed-english-v3",
input=["Hello this is a test", "skibidi"],
input_type="classification"
)
from obiguard import Obiguard
client = Obiguard(
obiguard_api_key="sk-obg***", # Your Obiguard API key
virtual_key="VIRTUAL_KEY",
)
embeddings = client.embeddings.create(
model="cohere.embed-english-v3",
input=["Hello this is a test", "skibidi"],
input_type="classification"
)
from openai import OpenAI
from obiguard import OBIGUARD_GATEWAY_URL, createHeaders
client = OpenAI(
api_key='NOT_REQUIRED',
base_url=OBIGUARD_GATEWAY_URL,
default_headers=createHeaders(
provider="openai",
obiguard_api_key="sk-obg******", # Your Obiguard API key
)
)
embeddings = client.embeddings.create(
model="cohere.embed-english-v3",
input=["Hello this is a test", "skibidi"],
input_type="classification"
)
curl --location 'https://gateway.obiguard.ai/v1/embeddings' \
--header 'Content-Type: application/json' \
--header 'x-obiguard-api-key: $OBIGUARD_API_KEY' \
--data-raw '{
"model": "cohere.embed-english-v3",
"input": ["Hello this is a test", "skibidi"],
"input_type": "classification"
}'
from obiguard import Obiguard
client = Obiguard(
obiguard_api_key="sk-obg***", # Your Obiguard API key
virtual_key="VIRTUAL_KEY",
)
embeddings = client.embeddings.create(
model="cohere.embed-english-v3",
input_type="image",
dimensions=256,
input=[
{
"image": {
"base64": "Data:image/webp;base64,UklGRkacAABXRUJQVlA4IDqcAACQggKdASqpAn8B....."
}
}
]
)
from obiguard import Obiguard
client = Obiguard(
obiguard_api_key="sk-obg***", # Your Obiguard API key
virtual_key="VIRTUAL_KEY",
)
embeddings = client.embeddings.create(
model="cohere.embed-english-v3",
input_type="image",
dimensions=256,
input=[
{
"image": {
"base64": "Data:image/webp;base64,UklGRkacAABXRUJQVlA4IDqcAACQggKdASqpAn8B....."
}
}
]
)
from openai import OpenAI
from obiguard import OBIGUARD_GATEWAY_URL, createHeaders
client = OpenAI(
api_key='NOT_REQUIRED',
base_url=OBIGUARD_GATEWAY_URL,
default_headers=createHeaders(
provider="openai",
obiguard_api_key="sk-obg******", # Your Obiguard API key
)
)
embeddings = client.embeddings.create(
model="cohere.embed-english-v3",
input_type="image",
dimensions=256,
input=[
{
image: {
base64: "Data:image/webp;base64,UklGRkacAABXRUJQVlA4IDqcAACQggKdASqpAn8B....."
}
}
]
)
curl --location 'https://gateway.obiguard.ai/v1/embeddings' \
--header 'Content-Type: application/json' \
--header 'x-obiguard-api-key: $OBIGUARD_API_KEY' \
--data-raw '{
"model": "cohere.embed-english-v3",
"input_type": "image",
"dimensions": 256,
"input": [
{
"image": {
"base64": "Data:image/webp;base64,UklGRkacAABXRUJQVlA4IDqcAACQggKdASqpAn8B....."
}
}
]
}'