diff --git a/docs/components/llms/models/gemini.mdx b/docs/components/llms/models/gemini.mdx
new file mode 100644
index 00000000..f020a2dd
--- /dev/null
+++ b/docs/components/llms/models/gemini.mdx
@@ -0,0 +1,33 @@
+---
+title: Gemini
+---
+
+To use Gemini model, you have to set the `GEMINI_API_KEY` environment variable. You can obtain the Gemini API key from the [Google AI Studio](https://aistudio.google.com/app/apikey)
+
+## Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
+os.environ["GEMINI_API_KEY"] = "your-api-key"
+
+config = {
+ "llm": {
+ "provider": "gemini",
+ "config": {
+ "model": "gemini-1.5-flash-latest",
+ "temperature": 0.2,
+ "max_tokens": 1500,
+ }
+ }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+## Config
+
+All available parameters for the `Gemini` config are present in [Master List of All Params in Config](../config).
\ No newline at end of file
diff --git a/docs/components/llms/overview.mdx b/docs/components/llms/overview.mdx
index d0fce6a2..d4a3c415 100644
--- a/docs/components/llms/overview.mdx
+++ b/docs/components/llms/overview.mdx
@@ -23,6 +23,7 @@ To view all supported llms, visit the [Supported LLMs](./models).
+
## Structured vs Unstructured Outputs
diff --git a/docs/mint.json b/docs/mint.json
index 97c6625b..7e5e8f57 100644
--- a/docs/mint.json
+++ b/docs/mint.json
@@ -93,7 +93,8 @@
"components/llms/models/litellm",
"components/llms/models/mistral_AI",
"components/llms/models/google_AI",
- "components/llms/models/aws_bedrock"
+ "components/llms/models/aws_bedrock",
+ "components/llms/models/gemini"
]
}
]
diff --git a/mem0/llms/gemini.py b/mem0/llms/gemini.py
new file mode 100644
index 00000000..a475226c
--- /dev/null
+++ b/mem0/llms/gemini.py
@@ -0,0 +1,154 @@
+import os
+from typing import Dict, List, Optional
+
+try:
+ import google.generativeai as genai
+ from google.generativeai import GenerativeModel
+ from google.generativeai.types import content_types
+except ImportError:
+ raise ImportError("The 'google-generativeai' library is required. Please install it using 'pip install google-generativeai'.")
+
+from mem0.configs.llms.base import BaseLlmConfig
+from mem0.llms.base import LLMBase
+
+
+class GeminiLLM(LLMBase):
+ def __init__(self, config: Optional[BaseLlmConfig] = None):
+ super().__init__(config)
+
+ if not self.config.model:
+ self.config.model = "gemini-1.5-flash-latest"
+
+ api_key = self.config.api_key or os.getenv("GEMINI_API_KEY")
+ genai.configure(api_key=api_key)
+ self.client = GenerativeModel(model_name=self.config.model)
+
+ def _parse_response(self, response, tools):
+ """
+ Process the response based on whether tools are used or not.
+
+ Args:
+ response: The raw response from API.
+ tools: The list of tools provided in the request.
+
+ Returns:
+ str or dict: The processed response.
+ """
+ if tools:
+ processed_response = {
+ "content": content if (content := response.candidates[0].content.parts[0].text) else None,
+ "tool_calls": [],
+ }
+
+ for part in response.candidates[0].content.parts:
+ if fn := part.function_call:
+ processed_response["tool_calls"].append(
+ {
+ "name": fn.name,
+ "arguments": {key:val for key, val in fn.args.items()},
+ }
+ )
+
+ return processed_response
+ else:
+ return response.candidates[0].content.parts[0].text
+
+ def _reformat_messages(self, messages : List[Dict[str, str]]):
+ """
+ Reformat messages for Gemini.
+
+ Args:
+ messages: The list of messages provided in the request.
+
+ Returns:
+ list: The list of messages in the required format.
+ """
+ new_messages = []
+
+ for message in messages:
+ if message["role"] == "system":
+ content = "THIS IS A SYSTEM PROMPT. YOU MUST OBEY THIS: " + message["content"]
+
+ else:
+ content = message["content"]
+
+ new_messages.append({"parts": content,
+ "role": "model" if message["role"] == "model" else "user"})
+
+ return new_messages
+
+ def _reformat_tools(self, tools: Optional[List[Dict]]):
+ """
+ Reformat tools for Gemini.
+
+ Args:
+ tools: The list of tools provided in the request.
+
+ Returns:
+ list: The list of tools in the required format.
+ """
+
+ def remove_additional_properties(data):
+ """Recursively removes 'additionalProperties' from nested dictionaries."""
+
+ if isinstance(data, dict):
+ filtered_dict = {
+ key: remove_additional_properties(value)
+ for key, value in data.items()
+ if not (key == "additionalProperties")
+ }
+ return filtered_dict
+ else:
+ return data
+
+ new_tools = []
+ if tools:
+ for tool in tools:
+ func = tool['function'].copy()
+ new_tools.append({"function_declarations":[remove_additional_properties(func)]})
+
+ return new_tools
+ else:
+ return None
+
+ def generate_response(
+ self,
+ messages: List[Dict[str, str]],
+ response_format=None,
+ tools: Optional[List[Dict]] = None,
+ tool_choice: str = "auto",
+ ):
+ """
+ Generate a response based on the given messages using Gemini.
+
+ Args:
+ messages (list): List of message dicts containing 'role' and 'content'.
+ response_format (str or object, optional): Format for the response. Defaults to "text".
+ tools (list, optional): List of tools that the model can call. Defaults to None.
+ tool_choice (str, optional): Tool choice method. Defaults to "auto".
+
+ Returns:
+ str: The generated response.
+ """
+
+ params = {
+ "temperature": self.config.temperature,
+ "max_output_tokens": self.config.max_tokens,
+ "top_p": self.config.top_p,
+ }
+
+ if response_format:
+ params["response_mime_type"] = "application/json"
+ params["response_schema"] = list[response_format]
+ if tool_choice:
+ tool_config = content_types.to_tool_config(
+ {"function_calling_config":
+ {"mode": tool_choice, "allowed_function_names": [tool['function']['name'] for tool in tools] if tool_choice == "any" else None}
+ })
+
+ response = self.client.generate_content(contents = self._reformat_messages(messages),
+ tools = self._reformat_tools(tools),
+ generation_config = genai.GenerationConfig(**params),
+ tool_config = tool_config)
+
+ return self._parse_response(response, tools)
diff --git a/mem0/utils/factory.py b/mem0/utils/factory.py
index 43f853c6..b7606c69 100644
--- a/mem0/utils/factory.py
+++ b/mem0/utils/factory.py
@@ -22,6 +22,7 @@ class LlmFactory:
"openai_structured": "mem0.llms.openai_structured.OpenAIStructuredLLM",
"anthropic": "mem0.llms.anthropic.AnthropicLLM",
"azure_openai_structured": "mem0.llms.azure_openai_structured.AzureOpenAIStructuredLLM",
+ "gemini": "mem0.llms.gemini.GeminiLLM",
}
@classmethod
diff --git a/tests/llms/test_gemini_llm.py b/tests/llms/test_gemini_llm.py
new file mode 100644
index 00000000..c6244aeb
--- /dev/null
+++ b/tests/llms/test_gemini_llm.py
@@ -0,0 +1,118 @@
+from unittest.mock import Mock, patch
+
+import pytest
+from google.generativeai import GenerationConfig
+from google.generativeai.types import content_types
+
+from mem0.configs.llms.base import BaseLlmConfig
+from mem0.llms.gemini import GeminiLLM
+
+
+@pytest.fixture
+def mock_gemini_client():
+ with patch("mem0.llms.gemini.GenerativeModel") as mock_gemini:
+ mock_client = Mock()
+ mock_gemini.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)
+ llm = GeminiLLM(config)
+ messages = [
+ {"role": "system", "content": "You are a helpful assistant."},
+ {"role": "user", "content": "Hello, how are you?"},
+ ]
+
+ mock_part = Mock(text="I'm doing well, thank you for asking!")
+ mock_content = Mock(parts=[mock_part])
+ mock_message = Mock(content=mock_content)
+ mock_response = Mock(candidates=[mock_message])
+ mock_gemini_client.generate_content.return_value = mock_response
+
+ response = llm.generate_response(messages)
+
+ 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": "Hello, how are you?", "role": "user"}
+ ],
+ generation_config = GenerationConfig(temperature=0.7, max_output_tokens=100, top_p=1.0),
+ tools = None,
+ tool_config = content_types.to_tool_config(
+ {"function_calling_config":
+ {"mode": 'auto', "allowed_function_names": None}
+ })
+ )
+ 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)
+ messages = [
+ {"role": "system", "content": "You are a helpful assistant."},
+ {"role": "user", "content": "Add a new memory: Today is a sunny day."},
+ ]
+ tools = [
+ {
+ "type": "function",
+ "function": {
+ "name": "add_memory",
+ "description": "Add a memory",
+ "parameters": {
+ "type": "object",
+ "properties": {"data": {"type": "string", "description": "Data to add to memory"}},
+ "required": ["data"],
+ },
+ },
+ }
+ ]
+
+ mock_tool_call = Mock()
+ mock_tool_call.name = "add_memory"
+ mock_tool_call.args = {"data": "Today is a sunny day."}
+
+ mock_part = Mock()
+ mock_part.function_call = mock_tool_call
+ mock_part.text="I've added the memory for you."
+
+ mock_content = Mock()
+ mock_content.parts=[mock_part]
+
+ mock_message = Mock()
+ mock_message.content=mock_content
+
+ mock_response = Mock(candidates=[mock_message])
+ mock_gemini_client.generate_content.return_value = mock_response
+
+ response = llm.generate_response(messages, tools=tools)
+
+ 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": [{
+ "name": "add_memory",
+ "description": "Add a memory",
+ "parameters": {
+ "type": "object",
+ "properties": {"data": {"type": "string", "description": "Data to add to memory"}},
+ "required": ["data"]
+ }
+ }]
+ }
+ ],
+ tool_config = content_types.to_tool_config(
+ {"function_calling_config":
+ {"mode": 'auto', "allowed_function_names": None}
+ })
+ )
+
+ assert response["content"] == "I've added the memory for you."
+ assert len(response["tool_calls"]) == 1
+ assert response["tool_calls"][0]["name"] == "add_memory"
+ assert response["tool_calls"][0]["arguments"] == {"data": "Today is a sunny day."}