Implemented Gemini (#1490)

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).

mem0/llms/gemini.py

@@ -0,0 +1,139 @@
+import os
+from typing import Dict, List, Optional
+
+import google.generativeai as genai
+from google.generativeai import GenerativeModel
+from google.generativeai.types import content_types
+
+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.
+        """
+        if tools:
+            new_tools = []
+
+            for tool in tools:
+                func = tool["function"].copy()
+                func["parameters"].pop("additionalProperties", None)
+                new_tools.append({"function_declarations":[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)

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