genai.extensions.langchain.embeddings module

pydantic model genai.extensions.langchain.embeddings.LangChainEmbeddingsInterface

Bases: BaseModel, Embeddings

Class representing the LangChainChatInterface for interacting with the LangChain chat API.

Example:

from genai import Client, Credentials
from genai.extensions.langchain import LangChainEmbeddingsInterface
from genai.text.embedding import TextEmbeddingParameters

client = Client(credentials=Credentials.from_env())
embeddings = LangChainEmbeddingsInterface(
    client=client,
    model_id="sentence-transformers/all-minilm-l6-v2",
    parameters=TextEmbeddingParameters(truncate_input_tokens=True)
)

embeddings.embed_query("Hello world!")
embeddings.embed_documents(["First document", "Second document"])

Config:

• extra: str = forbid

• protected_namespaces: tuple = ()

• arbitrary_types_allowed: bool = True

field client: Client [Required]

field execution_options: ModelLike[CreateExecutionOptions] | None = None

field model_id: str [Required]

field parameters: ModelLike[TextEmbeddingParameters] | None = None

async aembed_documents(texts)

Asynchronous Embed search documents

Parameters:

texts (List[str]) –

Return type:

list[list[float]]

async aembed_query(text)

Asynchronous Embed query text.

Parameters:

text (str) –

Return type:

List[float]

embed_documents(texts)

Embed search documents

Parameters:

texts (list[str]) –

Return type:

list[list[float]]

embed_query(text)

Embed query text.

Parameters:

text (str) –

Return type:

list[float]