Added Langchain Embedding Plugin

ads/llm/__init__.py

@@ -1,21 +1,24 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*--
 
-# Copyright (c) 2023 Oracle and/or its affiliates.
+# Copyright (c) 2025 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
 try:
 import langchain
-from ads.llm.langchain.plugins.llms.oci_data_science_model_deployment_endpoint import (
-OCIModelDeploymentVLLM,
-OCIModelDeploymentTGI,
-)
+
+from ads.llm.chat_template import ChatTemplates
 from ads.llm.langchain.plugins.chat_models.oci_data_science import (
 ChatOCIModelDeployment,
-ChatOCIModelDeploymentVLLM,
 ChatOCIModelDeploymentTGI,
+ChatOCIModelDeploymentVLLM,
+)
+from ads.llm.langchain.plugins.embeddings.oci_data_science_model_deployment_endpoint import (
+OCIDataScienceEmbedding,
+)
+from ads.llm.langchain.plugins.llms.oci_data_science_model_deployment_endpoint import (
+OCIModelDeploymentTGI,
+OCIModelDeploymentVLLM,
 )
-from ads.llm.chat_template import ChatTemplates
 except ImportError as ex:
 if ex.name == "langchain":
 raise ImportError(

ads/llm/langchain/plugins/embeddings/__init__.py

@@ -0,0 +1,4 @@
+#!/usr/bin/env python
+
+# Copyright (c) 2025 Oracle and/or its affiliates.
+# Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/

ads/llm/langchain/plugins/embeddings/oci_data_science_model_deployment_endpoint.py

@@ -0,0 +1,184 @@
+#!/usr/bin/env python
+
+# Copyright (c) 2025 Oracle and/or its affiliates.
+# Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
+
+from typing import Any, Callable, Dict, List, Mapping, Optional
+
+import requests
+from langchain_core.embeddings import Embeddings
+from langchain_core.language_models.llms import create_base_retry_decorator
+from pydantic import BaseModel, Field
+
+DEFAULT_HEADER = {
+"Content-Type": "application/json",
+}
+
+
+class TokenExpiredError(Exception):
+pass
+
+
+def _create_retry_decorator(llm) -> Callable[[Any], Any]:
+"""Creates a retry decorator."""
+errors = [requests.exceptions.ConnectTimeout, TokenExpiredError]
+decorator = create_base_retry_decorator(
+error_types=errors, max_retries=llm.max_retries
+)
+return decorator
+
+
+class OCIDataScienceEmbedding(BaseModel, Embeddings):
+"""Embedding model deployed on OCI Data Science Model Deployment.
+
+Example:
+
+.. code-block:: python
+
+from ads.llm import OCIDataScienceEmbedding
+
+embeddings = OCIDataScienceEmbedding(
+endpoint="https://modeldeployment.us-ashburn-1.oci.customer-oci.com/<md_ocid>/predict",
+)
+""" # noqa: E501
+
+auth: dict = Field(default_factory=dict, exclude=True)
+"""ADS auth dictionary for OCI authentication:
+https://accelerated-data-science.readthedocs.io/en/latest/user_guide/cli/authentication.html.
+This can be generated by calling `ads.common.auth.api_keys()`
+or `ads.common.auth.resource_principal()`. If this is not
+provided then the `ads.common.default_signer()` will be used."""
+
+endpoint: str = ""
+"""The uri of the endpoint from the deployed Model Deployment model."""
+
+model_kwargs: Optional[Dict] = None
+"""Keyword arguments to pass to the model."""
+
+endpoint_kwargs: Optional[Dict] = None
+"""Optional attributes (except for headers) passed to the request.post
+function.
+"""
+
+max_retries: int = 1
+"""The maximum number of retries to make when generating."""
+
+@property
+def _identifying_params(self) -> Mapping[str, Any]:
+"""Get the identifying parameters."""
+_model_kwargs = self.model_kwargs or {}
+return {
+**{"endpoint": self.endpoint},
+**{"model_kwargs": _model_kwargs},
+}
+
+def _embed_with_retry(self, **kwargs) -> Any:
+"""Use tenacity to retry the call."""
+retry_decorator = _create_retry_decorator(self)
+
+@retry_decorator
+def _completion_with_retry(**kwargs: Any) -> Any:
+try:
+response = requests.post(self.endpoint, **kwargs)
+response.raise_for_status()
+return response
+except requests.exceptions.HTTPError as http_err:
+if response.status_code == 401 and self._refresh_signer():
+raise TokenExpiredError() from http_err
+else:
+raise ValueError(
+f"Server error: {str(http_err)}. Message: {response.text}"
+) from http_err
+except Exception as e:
+raise ValueError(f"Error occurs by inference endpoint: {str(e)}") from e
+
+return _completion_with_retry(**kwargs)
+
+def _embedding(self, texts: List[str]) -> List[List[float]]:
+"""Call out to OCI Data Science Model Deployment Endpoint.
+
+Args:
+texts: A list of texts to embed.
+
+Returns:
+A list of list of floats representing the embeddings, or None if an
+error occurs.
+"""
+_model_kwargs = self.model_kwargs or {}
+body = self._construct_request_body(texts, _model_kwargs)
+request_kwargs = self._construct_request_kwargs(body)
+response = self._embed_with_retry(**request_kwargs)
+return self._proceses_response(response)
+
+def _construct_request_kwargs(self, body: Any) -> dict:
+"""Constructs the request kwargs as a dictionary."""
+from ads.model.common.utils import _is_json_serializable
+
+_endpoint_kwargs = self.endpoint_kwargs or {}
+headers = _endpoint_kwargs.pop("headers", DEFAULT_HEADER)
+return (
+dict(
+headers=headers,
+json=body,
+auth=self.auth.get("signer"),
+**_endpoint_kwargs,
+)
+if _is_json_serializable(body)
+else dict(
+headers=headers,
+data=body,
+auth=self.auth.get("signer"),
+**_endpoint_kwargs,
+)
+)
+
+def _construct_request_body(self, texts: List[str], params: dict) -> Any:
+"""Constructs the request body."""
+return {"input": texts}
+
+def _proceses_response(self, response: requests.Response) -> List[List[float]]:
+"""Extracts results from requests.Response."""
+try:
+res_json = response.json()
+embeddings = res_json["data"][0]["embedding"]
+except Exception as e:
+raise ValueError(
+f"Error raised by inference API: {e}.\nResponse: {response.text}"
+) from e
+return embeddings
+
+def embed_documents(
+self,
+texts: List[str],
+chunk_size: Optional[int] = None,
+) -> List[List[float]]:
+"""Compute doc embeddings using OCI Data Science Model Deployment Endpoint.
+
+Args:
+texts: The list of texts to embed.
+chunk_size: The chunk size defines how many input texts will
+be grouped together as request. If None, will use the
+chunk size specified by the class.
+
+Returns:
+List of embeddings, one for each text.
+"""
+results = []
+_chunk_size = (
+len(texts) if (not chunk_size or chunk_size > len(texts)) else chunk_size
+)
+for i in range(0, len(texts), _chunk_size):
+response = self._embedding(texts[i : i + _chunk_size])
+results.extend(response)
+return results
+
+def embed_query(self, text: str) -> List[float]:
+"""Compute query embeddings using OCI Data Science Model Deployment Endpoint.
+
+Args:
+text: The text to embed.
+
+Returns:
+Embeddings for the text.
+"""
+return self._embedding([text])[0]

docs/source/user_guide/large_language_model/langchain_models.rst

@@ -127,6 +127,26 @@ Chat models takes `chat messages <https://python.langchain.com/docs/concepts/#me
 print(chunk.content, end="")
 
 
+Embedding Models
+================
+
+You can also use embedding model that's hosted on a `OCI Data Science Model Deployment <https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-about.htm>`_.
+
+
+.. code-block:: python3
+
+from ads.llm import OCIDataScienceEmbedding
+
+# Create an instance of OCI Model Deployment Endpoint
+# Replace the endpoint uri with your own
+embeddings = OCIDataScienceEmbedding(
+endpoint="https://modeldeployment.us-ashburn-1.oci.customer-oci.com/<MD_OCID>/predict",
+)
+
+query = "Hello World!"
+embeddings.embed_query(query)
+
+
 Tool Calling
 ============
 

tests/unitary/with_extras/langchain/embeddings/__init__.py

@@ -0,0 +1,5 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*--
+
+# Copyright (c) 2025 Oracle and/or its affiliates.
+# Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/

tests/unitary/with_extras/langchain/embeddings/test_oci_model_deployment_endpoint.py

@@ -0,0 +1,60 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*--
+
+# Copyright (c) 2025 Oracle and/or its affiliates.
+# Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
+
+"""Test OCI Data Science Model Deployment Endpoint."""
+
+import pytest
+import sys
+from unittest.mock import MagicMock,
+
+if sys.version_info < (3, 9):
+pytest.skip(allow_module_level=True)
+
+from ads.llm import OCIDataScienceEmbedding
+
+
+@("ads.llm.OCIDataScienceEmbedding._embed_with_retry")
+def test_embed_documents(mock_embed_with_retry) -> None:
+"""Test valid call to oci model deployment endpoint."""
+expected_output = [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]]
+result = MagicMock()
+result.json = MagicMock(
+return_value={
+"data": [{"embedding": expected_output}],
+}
+)
+mock_embed_with_retry.return_value = result
+endpoint = "https://MD_OCID/predict"
+documents = ["Hello", "World"]
+
+embeddings = OCIDataScienceEmbedding(
+endpoint=endpoint,
+)
+
+output = embeddings.embed_documents(documents)
+assert output == expected_output
+
+
+@("ads.llm.OCIDataScienceEmbedding._embed_with_retry")
+def test_embed_query(mock_embed_with_retry) -> None:
+"""Test valid call to oci model deployment endpoint."""
+expected_output = [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]]
+result = MagicMock()
+result.json = MagicMock(
+return_value={
+"data": [{"embedding": expected_output}],
+}
+)
+mock_embed_with_retry.return_value = result
+endpoint = "https://MD_OCID/predict"
+query = "Hello world"
+
+embeddings = OCIDataScienceEmbedding(
+endpoint=endpoint,
+)
+
+output = embeddings.embed_query(query)
+assert output == expected_output[0]