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