155 lines
5.3 KiB
Python
155 lines
5.3 KiB
Python
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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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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