embed() Method

def embed(
    self,
    *,
    model: str,
    input: Union[str, List[str]],
    provider: Optional[str] = None,
    **kwargs
) -> EmbedResponse

# 1. Model name only (auto-detect provider)
client.embed(model="voyage-3", input="Text")

# 2. Provider prefix
client.embed(model="voyageai:voyage-3", input="Text")

# 3. With explicit provider parameter
client.embed(provider="voyageai", model="voyage-3", input="Text")

# Single text
client.embed(model="voyage-3", input="Single text")

# Multiple texts (batch)
client.embed(model="voyage-3", input=["Text 1", "Text 2", "Text 3"])

client.embed(
    provider="voyageai",
    model="voyage-3",
    input="Text"
)

# Voyage AI with input_type
client.embed(
    model="voyage-3",
    input="What is AI?",
    input_type="query"
)

# Gemini with dimensions
client.embed(
    model="gemini-embedding-001",
    input="Text",
    dimensions=512
)

# Per-request API key override
client.embed(
    model="voyage-3",
    input="Text",
    api_key="custom-api-key"
)

class EmbedResponse:
    embeddings: List[List[float]]  # List of embedding vectors
    dimensions: int                 # Embedding dimensions
    usage: Usage                    # Usage information

class Usage:
    tokens: int       # Total tokens processed
    cost: float       # Cost in USD
    latency: float    # Request latency in seconds

response = client.embed(
    model="voyage-3",
    input="Hello, world!"
)

print(response.embeddings[0][:5])  # [0.123, 0.456, ...]
print(response.dimensions)          # 1024
print(response.usage.cost)          # 0.0000002

texts = [
    "First document",
    "Second document",
    "Third document"
]

response = client.embed(
    model="voyage-3",
    input=texts
)

# Process each embedding
for i, embedding in enumerate(response.embeddings):
    print(f"Document {i+1}: {len(embedding)} dimensions")

print(f"Total cost: ${response.usage.cost:.6f}")

# Voyage AI with input_type
response = client.embed(
    model="voyage-3",
    input="What is machine learning?",
    input_type="query"  # vs "document"
)

# Nomic with task_type and dimensions
response = client.embed(
    model="nomic-embed-text-v1.5",
    input="Clustering sample",
    task_type="clustering",
    dimensions=256  # Matryoshka embeddings
)

# Cohere with truncate
response = client.embed(
    model="embed-v4.0",
    input="Very long text...",
    truncate="END"  # or "START" or "NONE"
)

from catsu.exceptions import CatsuError

try:
    response = client.embed(
        model="voyage-3",
        input="Text"
    )
except CatsuError as e:
    print(f"Error: {e}")