Embeddings

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Before implementing embeddings

When selecting an embeddings provider, there are several factors you can consider depending on your needs and preferences:

• Dataset size & domain specificity: size of the model training dataset and its relevance to the domain you want to embed. Larger or more domain-specific data generally produces better in-domain embeddings
• Inference performance: embedding lookup speed and end-to-end latency. This is a particularly important consideration for large scale production deployments
• Customization: options for continued training on private data, or specialization of models for very specific domains. This can improve performance on unique vocabularies

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How to get embeddings with Anthropic

Anthropic does not offer its own embedding model. One embeddings provider that has a wide variety of options and capabilities encompassing all of the above considerations is Voyage AI.

Voyage AI makes state-of-the-art embedding models and offers customized models for specific industry domains such as finance and healthcare, or bespoke fine-tuned models for individual customers.

The rest of this guide is for Voyage AI, but we encourage you to assess a variety of embeddings vendors to find the best fit for your specific use case.

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Available Models

Voyage recommends using the following text embedding models:

Additionally, the following multimodal embedding models are recommended:

Need help deciding which text embedding model to use? Check out the FAQ.

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Getting started with Voyage AI

To access Voyage embeddings:

1. Sign up on Voyage AI’s website
2. Obtain an API key
3. Set the API key as an environment variable for convenience:

export VOYAGE_API_KEY="<your secret key>"

You can obtain the embeddings by either using the official voyageai Python package or HTTP requests, as described below.

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Voyage Python Package

The voyageai package can be installed using the following command:

pip install -U voyageai

Then, you can create a client object and start using it to embed your texts:

import voyageai

vo = voyageai.Client()
# This will automatically use the environment variable VOYAGE_API_KEY.
# Alternatively, you can use vo = voyageai.Client(api_key="<your secret key>")

texts = ["Sample text 1", "Sample text 2"]

result = vo.embed(texts, model="voyage-3", input_type="document")
print(result.embeddings[0])
print(result.embeddings[1])

result.embeddings will be a list of two embedding vectors, each containing 1024 floating-point numbers. After running the above code, the two embeddings will be printed on the screen:

[0.02012746, 0.01957859, ...]  # embedding for "Sample text 1"
[0.01429677, 0.03077182, ...]  # embedding for "Sample text 2"

When creating the embeddings, you may also specify a few other arguments to the embed() function. You can read more about the specification here

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Voyage HTTP API

You can also get embeddings by requesting Voyage HTTP API. For example, you can send an HTTP request through the curl command in a terminal:

curl https://api.voyageai.com/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $VOYAGE_API_KEY" \
  -d '{
    "input": ["Sample text 1", "Sample text 2"],
    "model": "voyage-3"
  }'

The response you would get is a JSON object containing the embeddings and the token usage:

{
  "object": "list",
  "data": [
    {
      "embedding": [0.02012746, 0.01957859, ...],
      "index": 0
    },
    {
      "embedding": [0.01429677, 0.03077182, ...],
      "index": 1
    }
  ],
  "model": "voyage-3",
  "usage": {
    "total_tokens": 10
  }
}

You can read more about the embedding endpoint in the Voyage documentation

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AWS Marketplace

Voyage embeddings are also available on AWS Marketplace. Instructions for accessing Voyage on AWS are available here.

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Quickstart Example

Now that we know how to get embeddings, let’s see a brief example.

Suppose we have a small corpus of six documents to retrieve from

documents = [
    "The Mediterranean diet emphasizes fish, olive oil, and vegetables, believed to reduce chronic diseases.",
    "Photosynthesis in plants converts light energy into glucose and produces essential oxygen.",
    "20th-century innovations, from radios to smartphones, centered on electronic advancements.",
    "Rivers provide water, irrigation, and habitat for aquatic species, vital for ecosystems.",
    "Apple’s conference call to discuss fourth fiscal quarter results and business updates is scheduled for Thursday, November 2, 2023 at 2:00 p.m. PT / 5:00 p.m. ET.",
    "Shakespeare's works, like 'Hamlet' and 'A Midsummer Night's Dream,' endure in literature."
]

We will first use Voyage to convert each of them into an embedding vector

import voyageai

vo = voyageai.Client()

# Embed the documents
doc_embds = vo.embed(
    documents, model="voyage-3", input_type="document"
).embeddings

The embeddings will allow us to do semantic search / retrieval in the vector space. Given an example query,

query = "When is Apple's conference call scheduled?"

we convert it into an embedding, and conduct a nearest neighbor search to find the most relevant document based on the distance in the embedding space.

import numpy as np

# Embed the query
query_embd = vo.embed(
    [query], model="voyage-3", input_type="query"
).embeddings[0]

# Compute the similarity
# Voyage embeddings are normalized to length 1, therefore dot-product
# and cosine similarity are the same.
similarities = np.dot(doc_embds, query_embd)

retrieved_id = np.argmax(similarities)
print(documents[retrieved_id])

Note that we use input_type="document" and input_type="query" for embedding the document and query, respectively. More specification can be found here.

The output would be the 5th document, which is indeed the most relevant to the query:

Apple's conference call to discuss fourth fiscal quarter results and business updates is scheduled for Thursday, November 2, 2023 at 2:00 p.m. PT / 5:00 p.m. ET.

If you are looking for a detailed set of cookbooks on how to do RAG with embeddings, including vector databases, check out our RAG cookbook.

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FAQ

Represent the query for retrieving supporting documents: [your query]

Represent the document for retrieval: [your document]

Original: [-0.03955078, 0.006214142, -0.07446289, -0.039001465, 
          0.0046463013, 0.00030612946, -0.08496094, 0.03994751]
Binary:   [0, 1, 0, 0, 1, 1, 0, 1] → 01001101
uint8:    77
int8:     -51 (using offset binary)

import voyageai
import numpy as np

def embd_normalize(v: np.ndarray) -> np.ndarray:
    """
    Normalize embedding vectors to unit length.
    Raises ValueError if any row has zero norm.
    """
    row_norms = np.linalg.norm(v, axis=1, keepdims=True)
    if np.any(row_norms == 0):
        raise ValueError("Cannot normalize rows with a norm of zero.")
    return v / row_norms

# Initialize client
vo = voyageai.Client()

# Generate 1024D vectors
embd = vo.embed(['Sample text 1', 'Sample text 2'], 
               model='voyage-code-3').embeddings

# Truncate to 256D
short_dim = 256
resized_embd = embd_normalize(
    np.array(embd)[:, :short_dim]
).tolist()

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Pricing

Visit Voyage’s pricing page for the most up to date pricing details.