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Embeddings

Translate complex data into numerical vector representations


Input: Text, images, or audio

Output: Embeddings

Embedding models, often referred to as embeddings or embedding vectors, allow you to convert complex data into vectors while preserving meaningful relationships between them.

Embeddings are a powerful tool in the world of machine learning, acting as a bridge between complex data and the mathematical world that machine learning models operate in.

Data lives in its own world. For example, text is a sequence of words, images are a grid of pixels, and audio is a wave of sound. These are all very different from the numbers that machine learning models use.

Embedding models take these complex data types and transform them into numerical vectors. These vectors are like condensed summaries of the data, capturing its important aspects in a way that the model can understand.

By using these vectors, machine learning models can now reason about the data, compare different pieces of information, and perform tasks like classification, similarity search, and prediction.

info

The initialization code used in the following examples is outlined in detail on the client installation page.

Text Embeddings

Below is an example of how you would create text embeddings using the Cohere Embed-v3 model.

The Cohere Embed-v3 model requires an input_type parameter to be specified, which can be set using one of the following values:

  • search_document (default): For texts (documents) intended to be stored in a vector database.
  • search_query: For search queries to find the most relevant documents in a vector database.
  • classification: If the embeddings are used as input for a classification system.
  • clustering: If the embeddings are used for text clustering.
#################################################################################################################
# In this section, we set the user authentication, user and app ID, model details, and the text we want
# to provide as an input. Change these strings to run your own example.
#################################################################################################################

# Your PAT (Personal Access Token) can be found in the portal under Authentification
PAT = "YOUR_PAT_HERE"
# Specify the correct user_id/app_id pairings
# Since you're making inferences outside your app's scope
USER_ID = "cohere"
APP_ID = "embed"
# Change these to whatever model and text URL you want to use
MODEL_ID = "cohere-embed-english-v3_0"
MODEL_VERSION_ID = "e2dd848faf454fbda85c26cf89c4926e"
RAW_TEXT = "Give me an exotic yet tasty recipe for some noodle dish"
# To use a hosted text file, assign the URL variable
# TEXT_FILE_URL = "https://samples.clarifai.com/negative_sentence_12.txt"
# Or, to use a local text file, assign the location variable
# TEXT_FILE_LOCATION = "YOUR_TEXT_FILE_LOCATION_HERE"

############################################################################
# YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
############################################################################

from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel
from clarifai_grpc.grpc.api import resources_pb2, service_pb2, service_pb2_grpc
from clarifai_grpc.grpc.api.status import status_code_pb2
from google.protobuf.struct_pb2 import Struct

channel = ClarifaiChannel.get_grpc_channel()
stub = service_pb2_grpc.V2Stub(channel)

params = Struct()
params.update(
{
"input_type": "search_query"
}
)

metadata = (("authorization", "Key " + PAT),)

userDataObject = resources_pb2.UserAppIDSet(user_id=USER_ID, app_id=APP_ID)

# To use a local text file, uncomment the following lines
# with open(TEXT_FILE_LOCATION, "rb") as f:
# file_bytes = f.read()

post_model_outputs_response = stub.PostModelOutputs(
service_pb2.PostModelOutputsRequest(
user_app_id=userDataObject, # The userDataObject is created in the overview and is required when using a PAT
model_id=MODEL_ID,
version_id=MODEL_VERSION_ID, # This is optional. Defaults to the latest model version
inputs=[
resources_pb2.Input(
data=resources_pb2.Data(
text=resources_pb2.Text(
raw=RAW_TEXT
# url=TEXT_FILE_URL
# raw=file_bytes
)
)
)
],
model=resources_pb2.Model(
model_version=resources_pb2.ModelVersion(
output_info=resources_pb2.OutputInfo(params=params)
)
),
),
metadata=metadata,
)
if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
print(post_model_outputs_response.status)
raise Exception("Post model outputs failed, status: " + post_model_outputs_response.status.description )

# Uncomment this line to print the full Response JSON
# print(post_model_outputs_response)

# Since we have one input, one output will exist here
output = post_model_outputs_response.outputs[0].data.embeddings
print(output)
Text Output Example
[vector: 0.025470778346061707
vector: 0.027972102165222168
vector: -0.01801256835460663
vector: -0.025394519791007042
vector: 0.007034968119114637
vector: -0.0002650028618518263
vector: -0.0037653285544365644
vector: -0.0023831194266676903
vector: -0.03773335739970207
vector: 0.05649327486753464
vector: 0.01828710362315178
vector: 0.00324104237370193
vector: -0.015084192156791687
vector: -0.0007111228187568486
vector: -0.009479095228016376
vector: -0.016197584569454193
vector: 0.01801256835460663
vector: 0.017738033086061478
vector: 0.027041731402277946
vector: 0.005357252433896065
vector: 0.0050331479869782925
vector: 0.028963478282094002
vector: -0.012285456992685795
vector: -0.035171028226614
vector: 0.041058287024497986
vector: -0.030442919582128525
vector: -0.0278653372079134
vector: -0.00623805308714509
vector: 0.011621995829045773
vector: 0.001777807017788291
vector: -0.005467828828841448
vector: 0.01667039655148983
vector: 0.05164314806461334
vector: 0.018698906525969505
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JSON Output Example
status {
code: SUCCESS
description: "Ok"
req_id: "d03748f58ba35a376266f4935132521b"
}
outputs {
id: "a52203d9a2114428ac6ed23139e1afa9"
status {
code: SUCCESS
description: "Ok"
}
created_at {
seconds: 1702460385
nanos: 732703311
}
model {
id: "cohere-embed-english-v3_0"
name: "embed-english-v3_0"
created_at {
seconds: 1699267051
nanos: 239483000
}
app_id: "embed"
model_version {
id: "e2dd848faf454fbda85c26cf89c4926e"
created_at {
seconds: 1699269295
nanos: 601056000
}
status {
code: MODEL_TRAINED
description: "Model is trained and ready"
}
completed_at {
seconds: 1699269521
nanos: 395913000
}
visibility {
gettable: PUBLIC
}
app_id: "embed"
user_id: "cohere"
metadata {
}
}
user_id: "cohere"
model_type_id: "text-embedder"
visibility {
gettable: PUBLIC
}
modified_at {
seconds: 1701248637
nanos: 454591000
}
workflow_recommended {
}
}
input {
id: "3ce817f59bbe422d89d68b16dcd0b98b"
data {
text {
raw: "Give me an exotic yet tasty recipe for some noodle dish"
url: "https://samples.clarifai.com/placeholder.gif"
}
}
}
data {
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