feat: add Google embedding integration

docs/user-guides/configuration-guide.md

@@ -538,6 +538,7 @@ The following tables lists the supported embedding providers:
 | OpenAI               | `openai`               | `text-embedding-ada-002`, etc.     |
 | SentenceTransformers | `SentenceTransformers` | `all-MiniLM-L6-v2`, etc.           |
 | NVIDIA AI Endpoints  | `nvidia_ai_endpoints`  | `nv-embed-v1`, etc.                |
+| Google               | `google`               | `gemini-embedding-001`, etc.       |
 
 ```{note}
 You can use any of the supported models for any of the supported embedding providers.

nemoguardrails/embeddings/providers/__init__.py

@@ -18,7 +18,7 @@
 
 from typing import Optional, Type
 
-from . import fastembed, nim, openai, sentence_transformers
+from . import fastembed, google, nim, openai, sentence_transformers
 from .base import EmbeddingModel
 from .registry import EmbeddingProviderRegistry
 
@@ -68,6 +68,7 @@ def register_embedding_provider(
 register_embedding_provider(sentence_transformers.SentenceTransformerEmbeddingModel)
 register_embedding_provider(nim.NIMEmbeddingModel)
 register_embedding_provider(nim.NVIDIAAIEndpointsEmbeddingModel)
+register_embedding_provider(google.GoogleEmbeddingModel)
 
 
 def init_embedding_model(

nemoguardrails/embeddings/providers/google.py

@@ -0,0 +1,91 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import List
+
+from .base import EmbeddingModel
+
+
+class GoogleEmbeddingModel(EmbeddingModel):
+    """Embedding model using langchain_google_genai.
+
+    This class is a wrapper for using embedding models powered by Google AI (hosted in the Google Cloud).
+
+    To use, you must have either:
+
+        1. The ``GOOGLE_API_KEY`` environment variable set with your API key, or
+        2. Pass your API key using the google_api_key kwarg to the
+        GoogleGenerativeAIEmbeddings constructor.
+
+    Args:
+        embedding_model (str): The name of the embedding model to be used.
+
+    Attributes:
+        model: The name of the embedding model.
+        embedding_size (int): The size of the embeddings.
+    """
+
+    engine_name = "google"
+
+    def __init__(self, embedding_model: str, **kwargs):
+        try:
+            from langchain_google_genai import GoogleGenerativeAIEmbeddings
+
+        except ImportError:
+            raise ImportError(
+                "Could not import langchain_google_genai, please install it with "
+                "`pip install langchain-google-genai`."
+            )
+
+        self.model = embedding_model
+        self.document_embedder = GoogleGenerativeAIEmbeddings(
+            model=embedding_model, **kwargs
+        )
+
+        self.embedding_size_dict = {
+            "gemini-embedding-001": 3072,
+            "text-embedding-005": 768,
+            "text-multilingual-embedding-002": 768,
+        }
+
+        if self.model in self.embedding_size_dict:
+            self.embedding_size = self.embedding_size_dict[self.model]
+        else:
+            # Perform a first encoding to get the embedding size
+            self.embedding_size = len(self.encode(["test"])[0])
+
+    async def encode_async(self, documents: List[str]) -> List[List[float]]:
+        """Encode a list of documents into their corresponding sentence embeddings.
+
+        Args:
+            documents (List[str]): The list of documents to be encoded.
+
+        Returns:
+            List[List[float]]: The list of sentence embeddings, where each embedding is a list of floats.
+        """
+
+        result = await self.document_embedder.aembed_documents(documents)
+        return result
+
+    def encode(self, documents: List[str]) -> List[List[float]]:
+        """Encode a list of documents into their corresponding sentence embeddings.
+
+        Args:
+            documents (List[str]): The list of documents to be encoded.
+
+        Returns:
+            List[List[float]]: The list of sentence embeddings, where each embedding is a list of floats.
+        """
+        return self.document_embedder.embed_documents(documents)

tests/test_configs/with_google_embeddings/config.co

@@ -0,0 +1,12 @@
+define user ask capabilities
+  "What can you do?"
+  "What can you help me with?"
+  "tell me what you can do"
+  "tell me about you"
+
+define bot inform capabilities
+  "I am an AI assistant that helps answer questions."
+
+define flow
+  user ask capabilities
+  bot inform capabilities

tests/test_configs/with_google_embeddings/config.yml

@@ -0,0 +1,8 @@
+models:
+  - type: main
+    engine: openai
+    model: gpt-3.5-turbo-instruct
+
+  - type: embeddings
+    engine: google
+    model: gemini-embedding-001

tests/test_embeddings_google.py

@@ -0,0 +1,97 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+
+import pytest
+
+from nemoguardrails import LLMRails, RailsConfig
+
+try:
+    from nemoguardrails.embeddings.providers.google import GoogleEmbeddingModel
+except ImportError:
+    # Ignore this if running in test environment when langchain-google-genai not installed.
+    GoogleEmbeddingModel = None
+
+CONFIGS_FOLDER = os.path.join(os.path.dirname(__file__), ".", "test_configs")
+
+LIVE_TEST_MODE = os.environ.get("LIVE_TEST")
+
+
+@pytest.fixture
+def app():
+    """Load the configuration where we replace FastEmbed with Google."""
+    config = RailsConfig.from_path(
+        os.path.join(CONFIGS_FOLDER, "with_google_embeddings")
+    )
+
+    return LLMRails(config)
+
+
+@pytest.mark.skipif(not LIVE_TEST_MODE, reason="Not in live mode.")
+def test_custom_llm_registration(app):
+    assert isinstance(
+        app.llm_generation_actions.flows_index._model, GoogleEmbeddingModel
+    )
+
+
+@pytest.mark.skipif(not LIVE_TEST_MODE, reason="Not in live mode.")
+@pytest.mark.asyncio
+async def test_live_query():
+    config = RailsConfig.from_path(
+        os.path.join(CONFIGS_FOLDER, "with_google_embeddings")
+    )
+    app = LLMRails(config)
+
+    result = await app.generate_async(
+        messages=[{"role": "user", "content": "tell me what you can do"}]
+    )
+
+    assert result == {
+        "role": "assistant",
+        "content": "I am an AI assistant that helps answer questions.",
+    }
+
+
+@pytest.mark.skipif(not LIVE_TEST_MODE, reason="Not in live mode.")
+@pytest.mark.asyncio
+def test_live_query(app):
+    result = app.generate(
+        messages=[{"role": "user", "content": "tell me what you can do"}]
+    )
+
+    assert result == {
+        "role": "assistant",
+        "content": "I am an AI assistant that helps answer questions.",
+    }
+
+
+@pytest.mark.skipif(not LIVE_TEST_MODE, reason="Not in live mode.")
+def test_sync_embeddings():
+    model = GoogleEmbeddingModel("gemini-embedding-001")
+
+    result = model.encode(["test"])
+
+    assert len(result[0]) == 3072
+
+
+@pytest.mark.skipif(not LIVE_TEST_MODE, reason="Not in live mode.")
+@pytest.mark.asyncio
+async def test_async_embeddings():
+    model = GoogleEmbeddingModel("gemini-embedding-001")
+
+    result = await model.encode_async(["test"])
+
+    assert len(result[0]) == 3072