Embeddings

langchain_core.embeddings.embeddings.Embeddings

Bases: ABC

Interface for embedding models.

This is an interface meant for implementing text embedding models.

Text embedding models are used to map text to a vector (a point in n-dimensional space).

Texts that are similar will usually be mapped to points that are close to each other in this space. The exact details of what's considered "similar" and how "distance" is measured in this space are dependent on the specific embedding model.

This abstraction contains a method for embedding a list of documents and a method for embedding a query text. The embedding of a query text is expected to be a single vector, while the embedding of a list of documents is expected to be a list of vectors.

Usually the query embedding is identical to the document embedding, but the abstraction allows treating them independently.

In addition to the synchronous methods, this interface also provides asynchronous versions of the methods.

By default, the asynchronous methods are implemented using the synchronous methods; however, implementations may choose to override the asynchronous methods with an async native implementation for performance reasons.

METHOD	DESCRIPTION
embed_documents	Embed search docs.
embed_query	Embed query text.
aembed_documents	Asynchronous Embed search docs.
aembed_query	Asynchronous Embed query text.

embed_documents abstractmethod

embed_documents(texts: list[str]) -> list[list[float]]

Embed search docs.

PARAMETER	DESCRIPTION
texts	List of text to embed. TYPE: list[str]

RETURNS	DESCRIPTION
list[list[float]]	List of embeddings.

embed_query abstractmethod

embed_query(text: str) -> list[float]

Embed query text.

PARAMETER	DESCRIPTION
text	Text to embed. TYPE: str

RETURNS	DESCRIPTION
list[float]	Embedding.

aembed_documents async

aembed_documents(texts: list[str]) -> list[list[float]]

Asynchronous Embed search docs.

PARAMETER	DESCRIPTION
texts	List of text to embed. TYPE: list[str]

RETURNS	DESCRIPTION
list[list[float]]	List of embeddings.

aembed_query async

aembed_query(text: str) -> list[float]

Asynchronous Embed query text.

PARAMETER	DESCRIPTION
text	Text to embed. TYPE: str

RETURNS	DESCRIPTION
list[float]	Embedding.

langchain_core.embeddings.fake.DeterministicFakeEmbedding

Bases: Embeddings, BaseModel

Deterministic fake embedding model for unit testing purposes.

This embedding model creates embeddings by sampling from a normal distribution with a seed based on the hash of the text.

Warning

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

Instantiate

from langchain_core.embeddings import DeterministicFakeEmbedding

embed = DeterministicFakeEmbedding(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]

METHOD	DESCRIPTION
embed_documents	Embed search docs.
embed_query	Embed query text.
aembed_documents	Asynchronous Embed search docs.
aembed_query	Asynchronous Embed query text.

size instance-attribute

size: int

The size of the embedding vector.

embed_documents

embed_documents(texts: list[str]) -> list[list[float]]

Embed search docs.

PARAMETER	DESCRIPTION
texts	List of text to embed. TYPE: list[str]

RETURNS	DESCRIPTION
list[list[float]]	List of embeddings.

embed_query

embed_query(text: str) -> list[float]

Embed query text.

PARAMETER	DESCRIPTION
text	Text to embed. TYPE: str

RETURNS	DESCRIPTION
list[float]	Embedding.

aembed_documents async

aembed_documents(texts: list[str]) -> list[list[float]]

Asynchronous Embed search docs.

PARAMETER	DESCRIPTION
texts	List of text to embed. TYPE: list[str]

RETURNS	DESCRIPTION
list[list[float]]	List of embeddings.

aembed_query async

aembed_query(text: str) -> list[float]

Asynchronous Embed query text.

PARAMETER	DESCRIPTION
text	Text to embed. TYPE: str

RETURNS	DESCRIPTION
list[float]	Embedding.

langchain_core.embeddings.fake.FakeEmbeddings

Bases: Embeddings, BaseModel

Fake embedding model for unit testing purposes.

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

Warning

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]

METHOD	DESCRIPTION
embed_documents	Embed search docs.
embed_query	Embed query text.
aembed_documents	Asynchronous Embed search docs.
aembed_query	Asynchronous Embed query text.

size instance-attribute

size: int

The size of the embedding vector.

embed_documents

embed_documents(texts: list[str]) -> list[list[float]]

Embed search docs.

PARAMETER	DESCRIPTION
texts	List of text to embed. TYPE: list[str]

RETURNS	DESCRIPTION
list[list[float]]	List of embeddings.

embed_query

embed_query(text: str) -> list[float]

Embed query text.

PARAMETER	DESCRIPTION
text	Text to embed. TYPE: str

RETURNS	DESCRIPTION
list[float]	Embedding.

aembed_documents async

aembed_documents(texts: list[str]) -> list[list[float]]

Asynchronous Embed search docs.

PARAMETER	DESCRIPTION
texts	List of text to embed. TYPE: list[str]

RETURNS	DESCRIPTION
list[list[float]]	List of embeddings.

aembed_query async

aembed_query(text: str) -> list[float]

Asynchronous Embed query text.

PARAMETER	DESCRIPTION
text	Text to embed. TYPE: str

RETURNS	DESCRIPTION
list[float]	Embedding.