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llms/gemini.py
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201
llms/gemini.py
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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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from google import genai
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from google.genai import types
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except ImportError:
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raise ImportError("The 'google-genai' library is required. Please install it using 'pip install google-genai'.")
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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-2.0-flash"
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api_key = self.config.api_key or os.getenv("GOOGLE_API_KEY")
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self.client = genai.Client(api_key=api_key)
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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": None,
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"tool_calls": [],
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}
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# Extract content from the first candidate
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if response.candidates and response.candidates[0].content.parts:
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for part in response.candidates[0].content.parts:
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if hasattr(part, "text") and part.text:
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processed_response["content"] = part.text
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break
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# Extract function calls
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if response.candidates and response.candidates[0].content.parts:
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for part in response.candidates[0].content.parts:
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if hasattr(part, "function_call") and part.function_call:
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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": dict(fn.args) if fn.args else {},
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}
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)
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return processed_response
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else:
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if response.candidates and response.candidates[0].content.parts:
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for part in response.candidates[0].content.parts:
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if hasattr(part, "text") and part.text:
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return part.text
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return ""
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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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tuple: (system_instruction, contents_list)
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"""
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system_instruction = None
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contents = []
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for message in messages:
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if message["role"] == "system":
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system_instruction = message["content"]
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else:
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content = types.Content(
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parts=[types.Part(text=message["content"])],
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role=message["role"],
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)
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contents.append(content)
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return system_instruction, contents
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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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if tools:
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function_declarations = []
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for tool in tools:
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func = tool["function"].copy()
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cleaned_func = remove_additional_properties(func)
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function_declaration = types.FunctionDeclaration(
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name=cleaned_func["name"],
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description=cleaned_func.get("description", ""),
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parameters=cleaned_func.get("parameters", {}),
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)
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function_declarations.append(function_declaration)
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tool_obj = types.Tool(function_declarations=function_declarations)
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return [tool_obj]
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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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# Extract system instruction and reformat messages
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system_instruction, contents = self._reformat_messages(messages)
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# Prepare generation config
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config_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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# Add system instruction to config if present
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if system_instruction:
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config_params["system_instruction"] = system_instruction
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if response_format is not None and response_format["type"] == "json_object":
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config_params["response_mime_type"] = "application/json"
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if "schema" in response_format:
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config_params["response_schema"] = response_format["schema"]
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if tools:
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formatted_tools = self._reformat_tools(tools)
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config_params["tools"] = formatted_tools
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if tool_choice:
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if tool_choice == "auto":
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mode = types.FunctionCallingConfigMode.AUTO
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elif tool_choice == "any":
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mode = types.FunctionCallingConfigMode.ANY
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else:
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mode = types.FunctionCallingConfigMode.NONE
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tool_config = types.ToolConfig(
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function_calling_config=types.FunctionCallingConfig(
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mode=mode,
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allowed_function_names=(
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[tool["function"]["name"] for tool in tools] if tool_choice == "any" else None
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),
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)
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)
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config_params["tool_config"] = tool_config
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generation_config = types.GenerateContentConfig(**config_params)
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response = self.client.models.generate_content(
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model=self.config.model, contents=contents, config=generation_config
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)
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return self._parse_response(response, tools)
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