InfinityEmbeddings#

class langchain_community.embeddings.infinity.InfinityEmbeddings[source]#

Bases: BaseModel, Embeddings

Self-hosted embedding models for infinity package.

See michaelfeil/infinity This also works for text-embeddings-inference and other self-hosted openai-compatible servers.

Infinity is a package to interact with Embedding Models on michaelfeil/infinity

Example

from langchain_community.embeddings import InfinityEmbeddings
InfinityEmbeddings(
    model="BAAI/bge-small",
    infinity_api_url="http://localhost:7997",
)

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 client: Any = None#: Infinity client.

param infinity_api_url: str = 'http://localhost:7997'#: Endpoint URL to use.

param model: str [Required]#: Underlying Infinity model id.

async aembed_documents(texts: List[str]) → List[List[float]][source]#

Async call out to Infinity’s embedding endpoint.

Parameters:: texts (List[str]) – The list of texts to embed.
Returns:: List of embeddings, one for each text.
Return type:: List[List[float]]

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

Async call out to Infinity’s embedding endpoint.

Parameters:: text (str) – The text to embed.
Returns:: Embeddings for the text.
Return type:: List[float]

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

Call out to Infinity’s embedding endpoint.

Parameters:: texts (List[str]) – The list of texts to embed.
Returns:: List of embeddings, one for each text.
Return type:: List[List[float]]

embed_query(text: str) → List[float][source]#

Call out to Infinity’s embedding endpoint.

Parameters:: text (str) – The text to embed.
Returns:: Embeddings for the text.
Return type:: List[float]

Examples using InfinityEmbeddings

Infinity