get_embeddings

Generates embeddings for the given texts using the configured embedding model.

Available in Embedded and Python SDK. The JS/TS SDK handles embedding generation internally.

Embedded
Python SDK

get_embeddings(texts: Union[str, List[str]]) -> np.ndarray

Parameters

Parameter	Type	Description
`texts`	`Union[str, List[str]]`	Single text string or list of text strings to embed

Returns

np.ndarray: NumPy array of embeddings

If single text: 1-D array of shape (dimension,)
If list of texts: 2-D array of shape (num_texts, dimension)

Exceptions

RuntimeError

Throws if no embedding model is available

TypeError

Throws if embedding model type is not supported

Example Usage

# Single text embedding
embedding = store.get_embeddings("Hello, world!")
print(f"Embedding shape: {embedding.shape}")  # (384,)

# Multiple text embeddings
texts = ["First document", "Second document", "Third document"]
embeddings = store.get_embeddings(texts)
print(f"Embeddings shape: {embeddings.shape}")  # (3, 384)

get_embeddings(texts: Union[str, List[str]]) -> np.ndarray

Parameters

Parameter	Type	Description
`texts`	`Union[str, List[str]]`	Single text string or list of text strings to embed

Returns

np.ndarray: NumPy array of embeddings

If single text: 1-D array of shape (dimension,)
If list of texts: 2-D array of shape (num_texts, dimension)

Delegates to the configured LangChain Embeddings object for embedding generation.

Example Usage

embedding = store.get_embeddings("Hello, world!")
print(f"Embedding shape: {embedding.shape}")  # (384,)

texts = ["First document", "Second document"]
embeddings = store.get_embeddings(texts)
print(f"Embeddings shape: {embeddings.shape}")  # (2, 384)

Introduction

CyborgVectorStore

Types

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Example Usage

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