98 lines
3.1 KiB
Python
98 lines
3.1 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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except ImportError:
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raise ImportError(
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"The 'google-generativeai' library is required. Please install it using 'pip install google-generativeai'."
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)
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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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"""
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A wrapper for Google's Gemini language model, integrating it with the LLMBase class.
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"""
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def __init__(self, config: Optional[BaseLlmConfig] = None):
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"""
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Initializes the Gemini LLM with the provided configuration.
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Args:
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config (Optional[BaseLlmConfig]): Configuration object for the model.
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"""
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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 _reformat_messages(
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self, messages: List[Dict[str, str]]
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) -> List[Dict[str, str]]:
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"""
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Reformats messages to match the Gemini API's expected structure.
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Args:
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messages (List[Dict[str, str]]): A list of messages with 'role' and 'content' keys.
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Returns:
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List[Dict[str, str]]: Reformatted 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 = (
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"THIS IS A SYSTEM PROMPT. YOU MUST OBEY THIS: " + message["content"]
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)
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else:
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content = message["content"]
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new_messages.append(
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{
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"parts": content,
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"role": "model" if message["role"] == "model" else "user",
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}
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)
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return new_messages
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def generate_response(
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self, messages: List[Dict[str, str]], response_format: Optional[Dict] = None
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) -> str:
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"""
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Generates a response from Gemini based on the given conversation history.
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Args:
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messages (List[Dict[str, str]]): List of message dictionaries containing 'role' and 'content'.
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response_format (Optional[Dict]): Specifies the response format (e.g., JSON schema).
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Returns:
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str: The generated response as text.
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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 and response_format.get("type") == "json_object":
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params["response_mime_type"] = "application/json"
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if "schema" in response_format:
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params["response_schema"] = response_format["schema"]
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response = self.client.generate_content(
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contents=self._reformat_messages(messages),
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generation_config=genai.GenerationConfig(**params),
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)
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return response.candidates[0].content.parts[0].text
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