TinyAsyncOpenAIInfinityEmbeddingClient#

class langchain_community.embeddings.infinity.TinyAsyncOpenAIInfinityEmbeddingClient(host: str = 'http://localhost:7797/v1', aiosession: ClientSession | None = None)[source]#

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[source]#

Parameters:

host (str) –
aiosession (ClientSession | None) –

Return type:

None

async aembed(model: str, texts: List[str]) → List[List[float]][source]#

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]][source]#

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]]