OpenAIEmbeddings#

class langchain_community.embeddings.openai.OpenAIEmbeddings[source]#

Bases: BaseModel, Embeddings

Deprecated since version 0.0.9: Use :class:`~langchain_openai.OpenAIEmbeddings` instead.

OpenAI embedding models.

To use, you should have the openai python package installed, and the environment variable OPENAI_API_KEY set with your API key or pass it as a named parameter to the constructor.

Example

from langchain_community.embeddings import OpenAIEmbeddings
openai = OpenAIEmbeddings(openai_api_key="my-api-key")

In order to use the library with Microsoft Azure endpoints, you need to set the OPENAI_API_TYPE, OPENAI_API_BASE, OPENAI_API_KEY and OPENAI_API_VERSION. The OPENAI_API_TYPE must be set to ‘azure’ and the others correspond to the properties of your endpoint. In addition, the deployment name must be passed as the model parameter.

Example

import os

os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_BASE"] = "https://<your-endpoint.openai.azure.com/"
os.environ["OPENAI_API_KEY"] = "your AzureOpenAI key"
os.environ["OPENAI_API_VERSION"] = "2023-05-15"
os.environ["OPENAI_PROXY"] = "http://your-corporate-proxy:8080"

from langchain_community.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(
    deployment="your-embeddings-deployment-name",
    model="your-embeddings-model-name",
    openai_api_base="https://your-endpoint.openai.azure.com/",
    openai_api_type="azure",
)
text = "This is a test query."
query_result = embeddings.embed_query(text)

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 allowed_special: Literal['all'] | Set[str] = {}#
param chunk_size: int = 1000#

Maximum number of texts to embed in each batch

param default_headers: Mapping[str, str] | None = None#
param default_query: Mapping[str, object] | None = None#
param deployment: str | None = 'text-embedding-ada-002'#
param disallowed_special: Literal['all'] | Set[str] | Sequence[str] = 'all'#
param embedding_ctx_length: int = 8191#

The maximum number of tokens to embed at once.

param headers: Any = None#
param http_client: Any | None = None#

Optional httpx.Client.

param max_retries: int = 2#

Maximum number of retries to make when generating.

param model: str = 'text-embedding-ada-002'#
param model_kwargs: Dict[str, Any] [Optional]#

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

param openai_api_base: str | None = None (alias 'base_url')#

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

param openai_api_key: str | None = None (alias 'api_key')#

Automatically inferred from env var OPENAI_API_KEY if not provided.

param openai_api_type: str | None = None#
param openai_api_version: str | None = None (alias 'api_version')#

Automatically inferred from env var OPENAI_API_VERSION if not provided.

param openai_organization: str | None = None (alias 'organization')#

Automatically inferred from env var OPENAI_ORG_ID if not provided.

param openai_proxy: str | None = None#
param request_timeout: float | Tuple[float, float] | Any | None = None (alias 'timeout')#

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

param retry_max_seconds: int = 20#

Max number of seconds to wait between retries

param retry_min_seconds: int = 4#

Min number of seconds to wait between retries

param show_progress_bar: bool = False#

Whether to show a progress bar when embedding.

param skip_empty: bool = False#

Whether to skip empty strings when embedding or raise an error. Defaults to not skipping.

param 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

param 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.

async aembed_documents(texts: List[str], chunk_size: int | None = 0) → List[List[float]][source]#

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

Parameters:

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

  • chunk_size (int | None) – The chunk size of embeddings. If None, will use the chunk size specified by the class.

Returns:

List of embeddings, one for each text.

Return type:

List[List[float]]

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

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

Parameters:

text (str) – The text to embed.

Returns:

Embedding for the text.

Return type:

List[float]

embed_documents(texts: List[str], chunk_size: int | None = 0) → List[List[float]][source]#

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

Parameters:

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

  • chunk_size (int | None) – The chunk size of embeddings. If None, will use the chunk size specified by the class.

Returns:

List of embeddings, one for each text.

Return type:

List[List[float]]

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

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

Parameters:

text (str) – The text to embed.

Returns:

Embedding for the text.

Return type:

List[float]

classmethod validate_environment(values: Dict) → Dict[source]#

Validate that api key and python package exists in environment.

Parameters:

values (Dict)

Return type:

Dict

Examples using OpenAIEmbeddings