AnyscaleEmbeddings#

class langchain_community.embeddings.anyscale.AnyscaleEmbeddings[source]#

Bases: OpenAIEmbeddings

Anyscale Embeddings API.

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 anyscale_api_base: str = 'https://api.endpoints.anyscale.com/v1'#: Base URL path for API requests.

param anyscale_api_key: SecretStr = None#: AnyScale Endpoints API keys.

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 = 500#: 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 = 'thenlper/gte-large'#: Model name to use.

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 = False#: Set this to False for non-OpenAI implementations of the embeddings API

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]]#

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]#

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]]#

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]#

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 AnyscaleEmbeddings

Anyscale