Fix: Migrate Gemini Embeddings (#3002)

Co-authored-by: Dev-Khant <devkhant24@gmail.com>

docs/components/embedders/models/gemini.mdx

@@ -39,5 +39,5 @@ Here are the parameters available for configuring Gemini embedder:
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `model` | The name of the embedding model to use | `models/text-embedding-004` |
-| `embedding_dims` | Dimensions of the embedding model | `768` |
+| `embedding_dims` | Dimensions of the embedding model (output_dimensionality will be considered as embedding_dims, so please set embedding_dims accordingly) | `768` |
 | `api_key` | The Gemini API key | `None` |

mem0/embeddings/gemini.py

@@ -1,7 +1,7 @@
 import os
 from typing import Literal, Optional
 
-import google.generativeai as genai
+import google.genai as genai
 
 from mem0.configs.embeddings.base import BaseEmbedderConfig
 from mem0.embeddings.base import EmbeddingBase
@@ -12,23 +12,28 @@ class GoogleGenAIEmbedding(EmbeddingBase):
         super().__init__(config)
 
         self.config.model = self.config.model or "models/text-embedding-004"
-        self.config.embedding_dims = self.config.embedding_dims or 768
+        self.config.embedding_dims = self.config.embedding_dims or self.config.output_dimensionality or 768
 
         api_key = self.config.api_key or os.getenv("GOOGLE_API_KEY")
 
-        genai.configure(api_key=api_key)
+        if api_key:
+            self.client = genai.Client(api_key="api_key")
+        else:
+            self.client = genai.Client()
 
     def embed(self, text, memory_action: Optional[Literal["add", "search", "update"]] = None):
         """
         Get the embedding for the given text using Google Generative AI.
         Args:
             text (str): The text to embed.
-            memory_action (optional): The type of embedding to use. Must be one of "add", "search", or "update". Defaults to None.
+            memory_action (optional): The type of embedding to use. (Currently not used by Gemini for task_type)
         Returns:
             list: The embedding vector.
         """
         text = text.replace("\n", " ")
-        response = genai.embed_content(
+
+        response = self.client.models.embed_content(
             model=self.config.model, content=text, output_dimensionality=self.config.embedding_dims
         )
+
         return response["embedding"]

mem0/llms/gemini.py

@@ -49,16 +49,17 @@ class GeminiLLM(LLMBase):
             for part in candidate.content.parts:
                 fn = getattr(part, "function_call", None)
                 if fn:
-                    processed_response["tool_calls"].append({
+                    processed_response["tool_calls"].append(
-                        "name": fn.name,
+                        {
-                        "arguments": fn.args,
+                            "name": fn.name,
-                    })
+                            "arguments": fn.args,
+                        }
+                    )
 
             return processed_response
 
         return content
 
-
     def _reformat_messages(self, messages: List[Dict[str, str]]) -> List[types.Content]:
         """
         Reformat messages for Gemini using google.genai.types.
@@ -78,15 +79,11 @@ class GeminiLLM(LLMBase):
                 content = message["content"]
 
             new_messages.append(
-                types.Content(
+                types.Content(role="model" if message["role"] == "model" else "user", parts=[types.Part(text=content)])
-                    role="model" if message["role"] == "model" else "user",
-                    parts=[types.Part(text=content)]
-                )
             )
 
         return new_messages
 
-
     def _reformat_tools(self, tools: Optional[List[Dict]]):
         """
         Reformat tools for Gemini.
@@ -131,7 +128,6 @@ class GeminiLLM(LLMBase):
         tools: Optional[List[Dict]] = None,
         tool_choice: str = "auto",
     ):
-        
         """
         Generate a response based on the given messages using Gemini.
 
@@ -161,31 +157,22 @@ class GeminiLLM(LLMBase):
             tool_config = types.ToolConfig(
                 function_calling_config=types.FunctionCallingConfig(
                     mode=tool_choice.upper(),  # Assuming 'any' should become 'ANY', etc.
-                    allowed_function_names=[
+                    allowed_function_names=[tool["function"]["name"] for tool in tools]
-                        tool["function"]["name"] for tool in tools
+                    if tool_choice == "any"
-                    ] if tool_choice == "any" else None
+                    else None,
                 )
             )
 
-        print(f"Tool config: {tool_config}")
-        print(f"Params: {params}" )
-        print(f"Messages: {messages}")
-        print(f"Tools: {tools}")
-        print(f"Reformatted messages: {self._reformat_messages(messages)}")
-        print(f"Reformatted tools: {self._reformat_tools(tools)}")
-
         response = self.client_gemini.models.generate_content(
-                model=self.config.model,
+            model=self.config.model,
-                contents=self._reformat_messages(messages),
+            contents=self._reformat_messages(messages),
-                config=types.GenerateContentConfig(
+            config=types.GenerateContentConfig(
-                    temperature= self.config.temperature,
+                temperature=self.config.temperature,
-                    max_output_tokens= self.config.max_tokens,
+                max_output_tokens=self.config.max_tokens,
-                    top_p= self.config.top_p,
+                top_p=self.config.top_p,
-                    tools=self._reformat_tools(tools),
+                tools=self._reformat_tools(tools),
-                    tool_config=tool_config,
+                tool_config=tool_config,
-
+            ),
-                ),
+        )
-            )
-        print(f"Response test: {response}")
 
         return self._parse_response(response, tools)

tests/embeddings/test_gemini_emeddings.py

@@ -28,3 +28,29 @@ def test_embed_query(mock_genai, config):
 
     assert embedding == [0.1, 0.2, 0.3, 0.4]
     mock_genai.assert_called_once_with(model="test_model", content="Hello, world!", output_dimensionality=786)
+
+def test_embed_returns_empty_list_if_none(mock_genai, config):
+    mock_genai.return_value = None
+
+    embedder = GoogleGenAIEmbedding(config)
+    result = embedder.embed("test")
+
+    assert result == []
+    mock_genai.assert_called_once()
+
+
+def test_embed_raises_on_error(mock_genai, config):
+    mock_genai.side_effect = RuntimeError("Embedding failed")
+
+    embedder = GoogleGenAIEmbedding(config)
+
+    with pytest.raises(RuntimeError, match="Embedding failed"):
+        embedder.embed("some input")
+
+def test_config_initialization(config):
+    embedder = GoogleGenAIEmbedding(config)
+
+    assert embedder.config.api_key == "dummy_api_key"
+    assert embedder.config.model == "test_model"
+    assert embedder.config.embedding_dims == 786
+

tests/llms/test_gemini.py

@@ -1,8 +1,7 @@
 from unittest.mock import Mock, patch
 
 import pytest
-from google.generativeai import GenerationConfig
+from google.genai import types
-from google.generativeai.types import content_types
 
 from mem0.configs.llms.base import BaseLlmConfig
 from mem0.llms.gemini import GeminiLLM
@@ -10,14 +9,14 @@ from mem0.llms.gemini import GeminiLLM
 
 @pytest.fixture
 def mock_gemini_client():
-    with patch("mem0.llms.gemini.GenerativeModel") as mock_gemini:
+    with patch("mem0.llms.gemini.genai") as mock_client_class:
         mock_client = Mock()
-        mock_gemini.return_value = mock_client
+        mock_client_class.return_value = mock_client
         yield mock_client
 
 
 def test_generate_response_without_tools(mock_gemini_client: Mock):
-    config = BaseLlmConfig(model="gemini-1.5-flash-latest", temperature=0.7, max_tokens=100, top_p=1.0)
+    config = BaseLlmConfig(model="gemini-2.0-flash-latest", temperature=0.7, max_tokens=100, top_p=1.0)
     llm = GeminiLLM(config)
     messages = [
         {"role": "system", "content": "You are a helpful assistant."},
@@ -25,6 +24,15 @@ def test_generate_response_without_tools(mock_gemini_client: Mock):
     ]
 
     mock_part = Mock(text="I'm doing well, thank you for asking!")
+    mock_embedding = Mock()
+    mock_embedding.values = [0.1, 0.2, 0.3]
+
+    mock_response = Mock()
+    mock_response.candidates = [Mock()]
+    mock_response.candidates[0].content.parts = [Mock()]
+    mock_response.candidates[0].content.parts[0].text = "I'm doing well, thank you for asking!"
+
+    mock_gemini_client.models.generate_content.return_value = mock_response
     mock_content = Mock(parts=[mock_part])
     mock_message = Mock(content=mock_content)
     mock_response = Mock(candidates=[mock_message])
@@ -37,15 +45,24 @@ def test_generate_response_without_tools(mock_gemini_client: Mock):
             {"parts": "THIS IS A SYSTEM PROMPT. YOU MUST OBEY THIS: You are a helpful assistant.", "role": "user"},
             {"parts": "Hello, how are you?", "role": "user"},
         ],
-        generation_config=GenerationConfig(temperature=0.7, max_output_tokens=100, top_p=1.0),
+        config=types.GenerateContentConfig(
-        tools=None,
+              temperature=0.7, 
-        tool_config=content_types.to_tool_config(
+              max_output_tokens=100, 
-            {"function_calling_config": {"mode": "auto", "allowed_function_names": None}}
+              top_p=1.0,
-        ),
+              tools=None,
-    )
+              tool_config=types.ToolConfig(
+                function_calling_config=types.FunctionCallingConfig(
+                    allowed_function_names=None,
+                    mode="auto"
+                
+                )
+            )
+    )   )
+    
     assert response == "I'm doing well, thank you for asking!"
 
 
+
 def test_generate_response_with_tools(mock_gemini_client: Mock):
     config = BaseLlmConfig(model="gemini-1.5-flash-latest", temperature=0.7, max_tokens=100, top_p=1.0)
     llm = GeminiLLM(config)
@@ -89,28 +106,46 @@ def test_generate_response_with_tools(mock_gemini_client: Mock):
 
     mock_gemini_client.generate_content.assert_called_once_with(
         contents=[
-            {"parts": "THIS IS A SYSTEM PROMPT. YOU MUST OBEY THIS: You are a helpful assistant.", "role": "user"},
-            {"parts": "Add a new memory: Today is a sunny day.", "role": "user"},
-        ],
-        generation_config=GenerationConfig(temperature=0.7, max_output_tokens=100, top_p=1.0),
-        tools=[
             {
-                "function_declarations": [
+                "parts": "THIS IS A SYSTEM PROMPT. YOU MUST OBEY THIS: You are a helpful assistant.",
-                    {
+                "role": "user"
-                        "name": "add_memory",
+            },
-                        "description": "Add a memory",
+            {
-                        "parameters": {
+                "parts": "Add a new memory: Today is a sunny day.",
-                            "type": "object",
+                "role": "user"
-                            "properties": {"data": {"type": "string", "description": "Data to add to memory"}},
+            },
-                            "required": ["data"],
-                        },
-                    }
-                ]
-            }
         ],
-        tool_config=content_types.to_tool_config(
+        config=types.GenerateContentConfig(
-            {"function_calling_config": {"mode": "auto", "allowed_function_names": None}}
+            temperature=0.7,
-        ),
+            max_output_tokens=100,
+            top_p=1.0,
+            tools=[
+                types.Tool(
+                    function_declarations=[
+                        types.FunctionDeclaration(
+                            name="add_memory",
+                            description="Add a memory",
+                            parameters={
+                                "type": "object",
+                                "properties": {
+                                    "data": {
+                                        "type": "string",
+                                        "description": "Data to add to memory"
+                                    }
+                                },
+                                "required": ["data"]
+                            }
+                        )
+                    ]
+                )
+            ],
+            tool_config=types.ToolConfig(
+                function_calling_config=types.FunctionCallingConfig(
+                    allowed_function_names=None,
+                    mode="auto"
+                )
+            )
+        )
     )
 
     assert response["content"] == "I've added the memory for you."