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An embedding is a sequence of numbers (a vector) that represents the semantic meaning of content such as natural language, code, or other structured data. They are widely used in Clustering, Semantic search and retrieval, Recommendation engines, Retrieval-Augmented Generation (RAG)

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Code Snippet

from openai import OpenAI

BASE_URL = "https://{controlPlaneUrl}/api/llm"
API_KEY = "your-truefoundry-api-key"

# Configure OpenAI client with TrueFoundry settings
client = OpenAI(
    api_key=API_KEY,
    base_url=BASE_URL,
)

response = client.embeddings.create(
    model="openai-main/text-embedding-3-small",
    input="TrueFoundry is amazing!"
)

print(response.data[0].embedding)

​
Expected Output

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [-0.006929283495992422, -0.005336422007530928, -4.547132266452536e-5, -0.024047505110502243]
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "prompt_tokens": 5,
    "total_tokens": 5
  }
}

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Notes for Cohere Models

When using Cohere models via the embeddings API, you must include an additional field called input_type in the request. This field indicates the purpose of the embedding and must be one of the following:
  • search_query
  • search_document
  • classification
  • clustering
response = client.embeddings.create(
    model="cohere-main/embed-english-v3.0",
    input="Find similar documents about AI.",
    input_type="search_query"
)

print(response.data[0].embedding)

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Multimodal Embeddings

Note: This feature is only available for the Vertex AI multimodalembedding@001 model.
Vertex AI supports multimodal embeddings that can encode images and videos along with text. This is useful for applications that need to search across multiple modalities or find semantic similarity between text and visual content.

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Image Embeddings

Generate embeddings from images with optional text captions: 
from openai import OpenAI

BASE_URL = "https://{controlPlaneUrl}/api/llm"
API_KEY = "your-truefoundry-api-key"

client = OpenAI(
    api_key=API_KEY,
    base_url=BASE_URL,
)

response = client.embeddings.create(
    model="vertex-ai-main/multimodalembedding@001",
    input=[
        {
            "text": "A red sports car on a mountain road",
            "image": {
                "base64": "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNk+M9QDwADhgGAWjR9awAAAABJRU5ErkJggg==",
                # "url": "gs://your-bucket/path/to/image.jpg"  # Alternative: use GCS URL
            }
        }
    ]
)

print(response.data[0].image_embedding)
Expected Output: 
{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0234, -0.0145, 0.0567, ...],
      "image_embedding": [-0.0347, 0.0336, -0.0160, ...]
    }
  ],
  "model": "multimodalembedding@001",
  "usage": {
    "prompt_tokens": 0,
    "total_tokens": 0
  }
}

​
Video Embeddings

Generate embeddings from video segments with temporal control: 
from openai import OpenAI

BASE_URL = "https://{controlPlaneUrl}/api/llm"
API_KEY = "your-truefoundry-api-key"

client = OpenAI(
    api_key=API_KEY,
    base_url=BASE_URL,
)

response = client.embeddings.create(
    model="vertex-ai-main/multimodalembedding@001",
    input=[
        {
            "text": "A cooking tutorial showing pasta preparation",
            "video": {
                "base64": "AAAAGGZ0eXBpc29tAAACAGlzb21pc28yYXZjMW1wNDEAAAAIZnJlZQAACKBtZGF0AAAA...",
                "start_offset": 0,      # Start time in seconds
                "end_offset": 30,       # End time in seconds
                "interval": 10,         # Sample interval in seconds
                # "url": "gs://your-bucket/path/to/video.mp4"  # Alternative: use GCS URL
            }
        }
    ]
)

print(response.data[0].video_embeddings)
Expected Output: 
{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0234, -0.0145, 0.0567, ...],
      "video_embeddings": [
        {
          "object": "embedding",
          "embedding": [0.0189, -0.0201, 0.0423, ...],
          "index": 0,
          "start_offset": 0,
          "end_offset": 10
        },
        {
          "object": "embedding",
          "embedding": [0.0278, -0.0156, 0.0512, ...],
          "index": 1,
          "start_offset": 10,
          "end_offset": 20
        }
      ]
    }
  ],
  "model": "multimodalembedding@001",
  "usage": {
    "prompt_tokens": 0,
    "total_tokens": 0
  }
}