88 lines
3.1 KiB
Python
88 lines
3.1 KiB
Python
import os
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from typing import Dict, List, Optional, Union
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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.anthropic import AnthropicConfig
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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[Union[BaseLlmConfig, AnthropicConfig, Dict]] = None):
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# Convert to AnthropicConfig if needed
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if config is None:
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config = AnthropicConfig()
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elif isinstance(config, dict):
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config = AnthropicConfig(**config)
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elif isinstance(config, BaseLlmConfig) and not isinstance(config, AnthropicConfig):
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# Convert BaseLlmConfig to AnthropicConfig
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config = AnthropicConfig(
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model=config.model,
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temperature=config.temperature,
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api_key=config.api_key,
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max_tokens=config.max_tokens,
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top_p=config.top_p,
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top_k=config.top_k,
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enable_vision=config.enable_vision,
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vision_details=config.vision_details,
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http_client_proxies=config.http_client,
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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 = "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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**kwargs,
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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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**kwargs: Additional Anthropic-specific parameters.
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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 = self._get_supported_params(messages=messages, **kwargs)
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params.update(
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{
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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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}
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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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