`OpenAIEmbeddings`¶

Reference docs

This page contains reference documentation for OpenAIEmbeddings. See the docs for conceptual guides, tutorials, and examples on using OpenAIEmbeddings.

langchain_openai.embeddings.OpenAIEmbeddings ¶

Bases: BaseModel, Embeddings

OpenAI embedding model integration.

Setup

Install langchain_openai and set environment variable OPENAI_API_KEY.

pip install -U langchain_openai
export OPENAI_API_KEY="your-api-key"

Key init args — embedding params: model: Name of OpenAI model to use. dimensions: The number of dimensions the resulting output embeddings should have. Only supported in 'text-embedding-3' and later models.

Key init args — client params: api_key: OpenAI API key. organization: OpenAI organization ID. If not passed in will be read from env var OPENAI_ORG_ID. max_retries: Maximum number of retries to make when generating. request_timeout: Timeout for requests to OpenAI completion API

See full list of supported init args and their descriptions in the params section.

Instantiate

from langchain_openai import OpenAIEmbeddings

embed = OpenAIEmbeddings(
    model="text-embedding-3-large"
    # With the `text-embedding-3` class
    # of models, you can specify the size
    # of the embeddings you want returned.
    # dimensions=1024
)

Embed single text

input_text = "The meaning of life is 42"
vector = embeddings.embed_query("hello")
print(vector[:3])

[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]

Embed multiple texts

vectors = embeddings.embed_documents(["hello", "goodbye"])
# Showing only the first 3 coordinates
print(len(vectors))
print(vectors[0][:3])

2
[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]

Async

await embed.aembed_query(input_text)
print(vector[:3])

# multiple:
# await embed.aembed_documents(input_texts)

[-0.009100092574954033, 0.005071679595857859, -0.0029193938244134188]

METHOD	DESCRIPTION
`build_extra`	Build extra kwargs from additional params that were passed in.
`validate_environment`	Validate that api key and python package exists in environment.
`embed_documents`	Call out to OpenAI's embedding endpoint for embedding search docs.
`aembed_documents`	Call out to OpenAI's embedding endpoint async for embedding search docs.
`embed_query`	Call out to OpenAI's embedding endpoint for embedding query text.
`aembed_query`	Call out to OpenAI's embedding endpoint async for embedding query text.

dimensions `class-attribute` `instance-attribute` ¶

dimensions: int | None = None

The number of dimensions the resulting output embeddings should have.

Only supported in text-embedding-3 and later models.

openai_api_version `class-attribute` `instance-attribute` ¶

openai_api_version: str | None = Field(
    default_factory=from_env("OPENAI_API_VERSION", default=None), alias="api_version"
)

Automatically inferred from env var OPENAI_API_VERSION if not provided.

openai_api_base `class-attribute` `instance-attribute` ¶

openai_api_base: str | None = Field(
    alias="base_url", default_factory=from_env("OPENAI_API_BASE", default=None)
)

Base URL path for API requests, leave blank if not using a proxy or service emulator.

embedding_ctx_length `class-attribute` `instance-attribute` ¶

embedding_ctx_length: int = 8191

The maximum number of tokens to embed at once.

openai_api_key `class-attribute` `instance-attribute` ¶

openai_api_key: SecretStr | None = Field(
    alias="api_key", default_factory=secret_from_env("OPENAI_API_KEY", default=None)
)

Automatically inferred from env var OPENAI_API_KEY if not provided.

openai_organization `class-attribute` `instance-attribute` ¶

openai_organization: str | None = Field(
    alias="organization",
    default_factory=from_env(["OPENAI_ORG_ID", "OPENAI_ORGANIZATION"], default=None),
)

Automatically inferred from env var OPENAI_ORG_ID if not provided.

chunk_size `class-attribute` `instance-attribute` ¶

chunk_size: int = 1000

Maximum number of texts to embed in each batch

max_retries `class-attribute` `instance-attribute` ¶

max_retries: int = 2

Maximum number of retries to make when generating.

request_timeout `class-attribute` `instance-attribute` ¶

request_timeout: float | tuple[float, float] | Any | None = Field(
    default=None, alias="timeout"
)

Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or None.

tiktoken_enabled `class-attribute` `instance-attribute` ¶

tiktoken_enabled: bool = True

Set this to False for non-OpenAI implementations of the embeddings API, e.g. the --extensions openai extension for text-generation-webui

tiktoken_model_name `class-attribute` `instance-attribute` ¶

tiktoken_model_name: str | None = None

The model name to pass to tiktoken when using this class. Tiktoken is used to count the number of tokens in documents to constrain them to be under a certain limit. By default, when set to None, this will be the same as the embedding model name. However, there are some cases where you may want to use this Embedding class with a model name not supported by tiktoken. This can include when using Azure embeddings or when using one of the many model providers that expose an OpenAI-like API but with different models. In those cases, in order to avoid erroring when tiktoken is called, you can specify a model name to use here.

show_progress_bar `class-attribute` `instance-attribute` ¶

show_progress_bar: bool = False

Whether to show a progress bar when embedding.

model_kwargs `class-attribute` `instance-attribute` ¶

model_kwargs: dict[str, Any] = Field(default_factory=dict)

Holds any model parameters valid for create call not explicitly specified.

skip_empty `class-attribute` `instance-attribute` ¶

skip_empty: bool = False

Whether to skip empty strings when embedding or raise an error.

retry_min_seconds `class-attribute` `instance-attribute` ¶

retry_min_seconds: int = 4

Min number of seconds to wait between retries

retry_max_seconds `class-attribute` `instance-attribute` ¶

retry_max_seconds: int = 20

Max number of seconds to wait between retries

http_client `class-attribute` `instance-attribute` ¶

http_client: Any | None = None

Optional httpx.Client. Only used for sync invocations. Must specify http_async_client as well if you'd like a custom client for async invocations.

http_async_client `class-attribute` `instance-attribute` ¶

http_async_client: Any | None = None

Optional httpx.AsyncClient. Only used for async invocations. Must specify http_client as well if you'd like a custom client for sync invocations.

check_embedding_ctx_length `class-attribute` `instance-attribute` ¶

check_embedding_ctx_length: bool = True

Whether to check the token length of inputs and automatically split inputs longer than embedding_ctx_length.

build_extra `classmethod` ¶

build_extra(values: dict[str, Any]) -> Any

Build extra kwargs from additional params that were passed in.

validate_environment ¶

validate_environment() -> Self

Validate that api key and python package exists in environment.

embed_documents ¶

embed_documents(
    texts: list[str], chunk_size: int | None = None, **kwargs: Any
) -> list[list[float]]

Call out to OpenAI's embedding endpoint for embedding search docs.

PARAMETER	DESCRIPTION
`texts`	The list of texts to embed. TYPE: `list[str]`
`chunk_size`	The chunk size of embeddings. If `None`, will use the chunk size specified by the class. TYPE: `int \| None` DEFAULT: `None`
`kwargs`	Additional keyword arguments to pass to the embedding API. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`list[list[float]]`	List of embeddings, one for each text.

aembed_documents `async` ¶

aembed_documents(
    texts: list[str], chunk_size: int | None = None, **kwargs: Any
) -> list[list[float]]

Call out to OpenAI's embedding endpoint async for embedding search docs.

PARAMETER	DESCRIPTION
`texts`	The list of texts to embed. TYPE: `list[str]`
`chunk_size`	The chunk size of embeddings. If `None`, will use the chunk size specified by the class. TYPE: `int \| None` DEFAULT: `None`
`kwargs`	Additional keyword arguments to pass to the embedding API. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`list[list[float]]`	List of embeddings, one for each text.

embed_query ¶

embed_query(text: str, **kwargs: Any) -> list[float]

Call out to OpenAI's embedding endpoint for embedding query text.

PARAMETER	DESCRIPTION
`text`	The text to embed. TYPE: `str`
`kwargs`	Additional keyword arguments to pass to the embedding API. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`list[float]`	Embedding for the text.

aembed_query `async` ¶

aembed_query(text: str, **kwargs: Any) -> list[float]

Call out to OpenAI's embedding endpoint async for embedding query text.

PARAMETER	DESCRIPTION
`text`	The text to embed. TYPE: `str`
`kwargs`	Additional keyword arguments to pass to the embedding API. TYPE: `Any` DEFAULT: `{}`

RETURNS	DESCRIPTION
`list[float]`	Embedding for the text.

OpenAIEmbeddings¶

langchain_openai.embeddings.OpenAIEmbeddings ¶

dimensions class-attribute instance-attribute ¶

openai_api_version class-attribute instance-attribute ¶

openai_api_base class-attribute instance-attribute ¶

embedding_ctx_length class-attribute instance-attribute ¶

openai_api_key class-attribute instance-attribute ¶

openai_organization class-attribute instance-attribute ¶

chunk_size class-attribute instance-attribute ¶

max_retries class-attribute instance-attribute ¶

request_timeout class-attribute instance-attribute ¶

tiktoken_enabled class-attribute instance-attribute ¶

tiktoken_model_name class-attribute instance-attribute ¶

show_progress_bar class-attribute instance-attribute ¶

model_kwargs class-attribute instance-attribute ¶

skip_empty class-attribute instance-attribute ¶

retry_min_seconds class-attribute instance-attribute ¶

retry_max_seconds class-attribute instance-attribute ¶

http_client class-attribute instance-attribute ¶

http_async_client class-attribute instance-attribute ¶

check_embedding_ctx_length class-attribute instance-attribute ¶

build_extra classmethod ¶