OracleEmbeddings#

class langchain_community.embeddings.oracleai.OracleEmbeddings[source]#

Bases: BaseModel, Embeddings

Get Embeddings

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 conn: Any = None#: Embedding Parameters

param params: Dict[str, Any] [Required]#: Proxy

param proxy: str | None = None#

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]][source]#

Compute doc embeddings using an OracleEmbeddings. :param texts: The list of texts to embed.

Returns:: List of embeddings, one for each input text.
Parameters:: texts (List[str])
Return type:: List[List[float]]

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

Compute query embedding using an OracleEmbeddings. :param text: The text to embed.

Returns:: Embedding for the text.
Parameters:: text (str)
Return type:: List[float]

static load_onnx_model(conn: Connection, dir: str, onnx_file: str, model_name: str) → None[source]#

Load an ONNX model to Oracle Database. :param conn: Oracle Connection, :param dir: Oracle Directory, :param onnx_file: ONNX file name, :param model_name: Name of the model.

Parameters:

conn (Connection)
dir (str)
onnx_file (str)
model_name (str)

Return type:

None

Examples using OracleEmbeddings