langchain_community.embeddings.openai.OpenAIEmbeddings

class langchain_community.embeddings.openai.OpenAIEmbeddings

Bases: BaseModel, Embeddings

[Deprecated] 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)

Notes

Deprecated since version langchain-community==0.0.9.

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

Raises ValidationError if the input data cannot be parsed to form a valid model.

param allowed_special: Union[Literal['all'], Set[str]] = {}

param chunk_size: int = 1000

Maximum number of texts to embed in each batch

param default_headers: Optional[Mapping[str, str]] = None

param default_query: Optional[Mapping[str, object]] = None

param deployment: Optional[str] = 'text-embedding-ada-002'

param disallowed_special: Union[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: Optional[Any] = 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: Optional[str] = None (alias 'base_url')

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

param openai_api_key: Optional[str] = None (alias 'api_key')

Automatically inferred from env var OPENAI_API_KEY if not provided.

param openai_api_type: Optional[str] = None

param openai_api_version: Optional[str] = None (alias 'api_version')

Automatically inferred from env var OPENAI_API_VERSION if not provided.

param openai_organization: Optional[str] = None (alias 'organization')

Automatically inferred from env var OPENAI_ORG_ID if not provided.

param openai_proxy: Optional[str] = None

param request_timeout: Optional[Union[float, Tuple[float, float], Any]] = 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: Optional[str] = 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: Optional[int] = 0) → List[List[float]]

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

Parameters

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

• chunk_size (Optional[int]) – 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]

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]

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Parameters

• _fields_set (Optional[SetStr]) –

• values (Any) –

Return type

Model

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

• include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model

• exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include

• update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

• deep (bool) – set to True to make a deep copy of the model

• self (Model) –

Returns

new model instance

Return type

Model

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters

• include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

• exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

• by_alias (bool) –

• skip_defaults (Optional[bool]) –

• exclude_unset (bool) –

• exclude_defaults (bool) –

• exclude_none (bool) –

Return type

DictStrAny

embed_documents(texts: List[str], chunk_size: Optional[int] = 0) → List[List[float]]

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

Parameters

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

• chunk_size (Optional[int]) – 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]

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 from_orm(obj: Any) → Model

Parameters

obj (Any) –

Return type

Model

json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

Parameters

• include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

• exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

• by_alias (bool) –

• skip_defaults (Optional[bool]) –

• exclude_unset (bool) –

• exclude_defaults (bool) –

• exclude_none (bool) –

• encoder (Optional[Callable[[Any], Any]]) –

• models_as_dict (bool) –

• dumps_kwargs (Any) –

Return type

unicode

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model

Parameters

• path (Union[str, Path]) –

• content_type (unicode) –

• encoding (unicode) –

• proto (Protocol) –

• allow_pickle (bool) –

Return type

Model

classmethod parse_obj(obj: Any) → Model

Parameters

obj (Any) –

Return type

Model

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model

Parameters

• b (Union[str, bytes]) –

• content_type (unicode) –

• encoding (unicode) –

• proto (Protocol) –

• allow_pickle (bool) –

Return type

Model

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny

Parameters

• by_alias (bool) –

• ref_template (unicode) –

Return type

DictStrAny

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode

Parameters

• by_alias (bool) –

• ref_template (unicode) –

• dumps_kwargs (Any) –

Return type

unicode

classmethod update_forward_refs(**localns: Any) → None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

Parameters

localns (Any) –

Return type

None

classmethod validate(value: Any) → Model

Parameters

value (Any) –

Return type

Model

Examples using OpenAIEmbeddings

• !pip install bs4

• Activeloop Deep Lake

• Agents

• Alibaba Cloud OpenSearch

• Analysis of Twitter the-algorithm source code with LangChain, GPT4 and Activeloop’s Deep Lake

• AnalyticDB

• Atlas

• AutoGPT

• Azure Cognitive Search

• Azure OpenAI

• AzureOpenAI

• BabyAGI User Guide

• BabyAGI with Tools

• Baidu Cloud VectorSearch

• Caching

• Cassandra

• Chroma

• ClickHouse

• Cohere Reranker

• Combine agents and vector stores

• Confident

• Conversational Retrieval Agent

• Custom Agent with PlugIn Retrieval

• Custom agent with tool retrieval

• Deep Lake

• DingoDB

• DocArray HnswSearch

• DocArray InMemorySearch

• DocArray Retriever

• Docugami

• Document Comparison

• Elasticsearch

• Epsilla

• Faiss

• Few-shot examples for chat models

• First we add a step to load memory

• Generative Agents in LangChain

• Hologres

• Improve document indexing with HyDE

• Indexing

• LLM Caching integrations

• LOTR (Merger Retriever)

• LanceDB

• Loading documents from a YouTube url

• Loading from LangChainHub

• Meilisearch

• Memory in the Multi-Input Chain

• Milvus

• MongoDB Atlas

• MultiQueryRetriever

• MultiVector Retriever

• MyScale

• Neo4j Vector Index

• OpenAI

• OpenSearch

• PGVector

• Parent Document Retriever

• Perform context-aware text splitting

• Pinecone

• Pinecone Hybrid Search

• Plug-and-Plai

• Postgres Embedding

• Psychic

• QA using Activeloop’s DeepLake

• Qdrant

• Question Answering

• RePhraseQueryRetriever

• Redis

• Retrieve as you generate with FLARE

• Retrieve from vector stores directly

• Rockset

• SQL

• SVM

• SalesGPT - Your Context-Aware AI Sales Assistant With Knowledge Base

• Select by maximal marginal relevance (MMR)

• Set env var OPENAI_API_KEY or load from a .env file

• Set env var OPENAI_API_KEY or load from a .env file:

• SingleStoreDB

• StarRocks

• Structure answers with OpenAI functions

• Supabase

• Supabase (Postgres)

• Tigris

• Typesense

• USearch

• Use LangChain, GPT and Activeloop’s Deep Lake to work with code base

• Vectorstore

• Weaviate

• WebResearchRetriever

• Xata

• Xata chat memory

• Zilliz

• kNN

• scikit-learn