65 lines
2.2 KiB
Python
65 lines
2.2 KiB
Python
import os
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from typing import Dict, List, Optional
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try:
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import anthropic
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except ImportError:
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raise ImportError("The 'anthropic' library is required. Please install it using 'pip install anthropic'.")
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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 AnthropicLLM(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 = "claude-3-5-sonnet-20240620"
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api_key = self.config.api_key or os.getenv("ANTHROPIC_API_KEY")
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self.client = anthropic.Anthropic(api_key=api_key)
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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 Anthropic.
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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 of 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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# Separate system message from other messages
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system_message = ""
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filtered_messages = []
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for message in messages:
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if message['role'] == 'system':
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system_message = message['content']
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else:
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filtered_messages.append(message)
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params = {
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"model": self.config.model,
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"messages": filtered_messages,
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"system": system_message,
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"temperature": self.config.temperature,
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"max_tokens": self.config.max_tokens,
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"top_p": self.config.top_p,
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}
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if tools: # TODO: Remove tools if no issues found with new memory addition logic
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params["tools"] = tools
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params["tool_choice"] = tool_choice
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response = self.client.messages.create(**params)
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return response.content[0].text
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