langchain.embeddings.localai.LocalAIEmbeddings

class langchain.embeddings.localai.LocalAIEmbeddings(*, client: Any = None, model: str = 'text-embedding-ada-002', deployment: str = 'text-embedding-ada-002', openai_api_version: Optional[str] = None, openai_api_base: Optional[str] = None, openai_proxy: Optional[str] = None, embedding_ctx_length: int = 8191, openai_api_key: Optional[str] = None, openai_organization: Optional[str] = None, allowed_special: Union[Literal['all'], Set[str]] = {}, disallowed_special: Union[Literal['all'], Set[str], Sequence[str]] = 'all', chunk_size: int = 1000, max_retries: int = 6, request_timeout: Optional[Union[float, Tuple[float, float]]] = None, headers: Any = None, show_progress_bar: bool = False, model_kwargs: Dict[str, Any] = None)

Bases: BaseModel, Embeddings

LocalAI embedding models.

To use, you should have the openai python package installed, and the environment variable OPENAI_API_KEY set to a random string. You need to specify OPENAI_API_BASE to point to your LocalAI service endpoint.

Example

from langchain.embeddings import LocalAIEmbeddings
openai = LocalAIEmbeddings(
    openai_api_key="random-key",
    openai_api_base="http://localhost:8080"
)

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 deployment: 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 max_retries: int = 6

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

param openai_api_key: Optional[str] = None

param openai_api_version: Optional[str] = None

param openai_organization: Optional[str] = None

param openai_proxy: Optional[str] = None

param request_timeout: Optional[Union[float, Tuple[float, float]]] = None

Timeout in seconds for the LocalAI request.

param show_progress_bar: bool = False

Whether to show a progress bar when embedding.

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

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

Parameters

• texts – The list of texts to embed.

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

Returns

List of embeddings, one for each text.

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

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

Parameters

text – The text to embed.

Returns

Embedding for the text.

validator build_extra  »  all fields

Build extra kwargs from additional params that were passed in.

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

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

Parameters

• texts – The list of texts to embed.

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

Returns

List of embeddings, one for each text.

embed_query(text: str) → List[float]

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

Parameters

text – The text to embed.

Returns

Embedding for the text.

validator validate_environment  »  all fields

Validate that api key and python package exists in environment.

model Config

Bases: object

Configuration for this pydantic object.

extra = 'forbid'

Examples using LocalAIEmbeddings

• LocalAI