HuggingFaceInferenceAPIEmbeddings

class langchain_community.embeddings.huggingface.HuggingFaceInferenceAPIEmbeddings

Bases: BaseModel, Embeddings

Embed texts using the HuggingFace API.

Requires a HuggingFace Inference API key and a model name.

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 additional_headers: Dict[str, str] = {}

Pass additional headers to the requests library if needed.

param api_key: SecretStr [Required]

Your API key for the HuggingFace Inference API.

param api_url: str | None = None

Custom inference endpoint url. None for using default public url.

param model_name: str = 'sentence-transformers/all-MiniLM-L6-v2'

The name of the model to use for text embeddings.

async aembed_documents(texts: list[str]) → list[list[float]]

Asynchronous Embed search docs.

Parameters:

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

Returns:

List of embeddings.

Return type:

list[list[float]]

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

Asynchronous Embed query text.

Parameters:

text (str) – Text to embed.

Returns:

Embedding.

Return type:

list[float]

embed_documents(texts: List[str]) → List[List[float]]

Get the embeddings for a list of texts.

Parameters:

texts (Documents) – A list of texts to get embeddings for.

Returns:

Embedded texts as List[List[float]], where each inner List[float]

corresponds to a single input text.

Return type:

List[List[float]]

Example

from langchain_community.embeddings import (
    HuggingFaceInferenceAPIEmbeddings,
)

hf_embeddings = HuggingFaceInferenceAPIEmbeddings(
    api_key="your_api_key",
    model_name="sentence-transformers/all-MiniLM-l6-v2"
)
texts = ["Hello, world!", "How are you?"]
hf_embeddings.embed_documents(texts)

embed_query(text: str) → List[float]

Compute query embeddings using a HuggingFace transformer model.

Parameters:

text (str) – The text to embed.

Returns:

Embeddings for the text.

Return type:

List[float]

Examples using HuggingFaceInferenceAPIEmbeddings

• Hugging Face