langchain.embeddings.localai.LocalAIEmbeddings¶
class langchain.embeddings.localai.LocalAIEmbeddings[source]¶
Bases: BaseModel, Embeddings
LocalAI embedding models.
Since LocalAI and OpenAI have 1:1 compatibility between APIs, this class
uses the openai Python package’s openai.Embedding as its client.
Thus, you should have the openai python package installed, and defeat
the environment variable OPENAI_API_KEY by setting to a random string.
You also 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-string",
    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¶