TinyAsyncOpenAIInfinityEmbeddingClient

class langchain_community.embeddings.infinity.TinyAsyncOpenAIInfinityEmbeddingClient(

host: str = 'http://localhost:7797/v1',

aiosession: ClientSession | None = None,

)

Helper tool to embed Infinity.

It is not a part of Langchain’s stable API, direct use discouraged.

Example

mini_client = TinyAsyncInfinityEmbeddingClient(
)
embeds = mini_client.embed(
    model="BAAI/bge-small",
    text=["doc1", "doc2"]
)
# or
embeds = await mini_client.aembed(
    model="BAAI/bge-small",
    text=["doc1", "doc2"]
)

Methods

__init__([host, aiosession])
aembed(model, texts)	call the embedding of model, async method
embed(model, texts)	call the embedding of model

Parameters:

• host (str)

• aiosession (ClientSession | None)

__init__(

host: str = 'http://localhost:7797/v1',

aiosession: ClientSession | None = None,

) → None

Parameters:

• host (str)

• aiosession (ClientSession | None)

Return type:

None

async aembed(

model: str,

texts: List[str],

) → List[List[float]]

call the embedding of model, async method

Parameters:

• model (str) – to embedding model

• texts (List[str]) – List of sentences to embed.

Returns:

List of vectors for each sentence

Return type:

List[List[float]]

embed(

model: str,

texts: List[str],

) → List[List[float]]

call the embedding of model

Parameters:

• model (str) – to embedding model

• texts (List[str]) – List of sentences to embed.

Returns:

List of vectors for each sentence

Return type:

List[List[float]]