FakeEmbeddings#

class langchain_core.embeddings.fake.FakeEmbeddings[source]#

Bases: Embeddings, BaseModel

Fake embedding model for unit testing purposes.

This embedding model creates embeddings by sampling from a normal distribution.

Do not use this outside of testing, as it is not a real embedding model.

Instantiate:
from langchain_core.embeddings import FakeEmbeddings
embed = FakeEmbeddings(size=100)
Embed single text:
input_text = "The meaning of life is 42"
vector = embed.embed_query(input_text)
print(vector[:3])

[-0.700234640213188, -0.581266257710429, -1.1328482266445354]
Embed multiple texts:
input_texts = ["Document 1...", "Document 2..."]
vectors = embed.embed_documents(input_texts)
print(len(vectors))
# The first 3 coordinates for the first vector
print(vectors[0][:3])

2
[-0.5670477847544458, -0.31403828652395727, -0.5840547508955257]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

param size: int [Required]#

The size of the embedding vector.

async aembed_documents(texts: list[str]) → list[list[float]]#

Asynchronous Embed search docs.

Parameters:

texts (list[str]) – List of text to embed.

Returns:

List of embeddings.

Return type:

list[list[float]]

async aembed_query(text: str) → list[float]#

Asynchronous Embed query text.

Parameters:

text (str) – Text to embed.

Returns:

Embedding.

Return type:

list[float]

embed_documents(texts: list[str]) → list[list[float]][source]#

Embed search docs.

Parameters:

texts (list[str]) – List of text to embed.

Returns:

List of embeddings.

Return type:

list[list[float]]

embed_query(text: str) → list[float][source]#

Embed query text.

Parameters:

text (str) – Text to embed.

Returns:

Embedding.

Return type:

list[float]

Examples using FakeEmbeddings