172 lines
6.3 KiB
Python
172 lines
6.3 KiB
Python
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import os
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import anthropic
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from .base_model import ModelInterface
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from dotenv import load_dotenv
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import sys
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import json
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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load_dotenv()
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class ClaudeModel(ModelInterface):
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"""DeepSeek model implementation.
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Attributes:
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model (str): The name of the DeepSeek model to use.
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"""
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def __init__(self, my_api_key=None, model_name="claude-3-5-sonnet-20241022"):
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"""Initialize the DeepSeek model.
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Args:
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api_key (str): The API key to use for the DeepSeek model.
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model_name (str): The name of the DeepSeek model to use.
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Example:
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>>> deepseek_model = DeepSeekModel()
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"""
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self.model_name = model_name
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if my_api_key:
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self.client = client = anthropic.Anthropic(
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# defaults to os.environ.get("ANTHROPIC_API_KEY")
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api_key=my_api_key,
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)
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else:
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self.client = anthropic.Anthropic(
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api_key=os.environ.get("ANTHROPIC_API_KEY")
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)
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# -------------------------------------------------------------------
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# Helper to generate a response to a given prompt using the OpenAI API.
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# -------------------------------------------------------------------
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def generate_response(self, prompt, **kwargs):
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"""Generate a response to a given prompt using the DeepSeek API.
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Args:
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prompt (str): The prompt to generate a response to.
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**kwargs: Additional keyword arguments.
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Returns:
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str: The generated response.
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Example:
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>>> deepseek_model = DeepSeekModel()
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>>> response = deepseek_model.generate_response("What is the weather in Tokyo?")
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"""
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if isinstance(prompt, str):
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return self.generate_response_from_string(prompt, **kwargs)
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elif isinstance(prompt, list[dict]):
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return self.generate_response_dictionary(prompt)
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# -------------------------------------------------------------------
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# Helper to generate a response to a given prompt using a list of dictionaries
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# -------------------------------------------------------------------
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def generate_response_dictionary(self, prompt: list[dict]) -> str:
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"""Generate a response to a given prompt using a list of dictionaries.
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Args:
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prompt (list[dict]): The prompt to generate a response to.
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Returns:
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str: The generated response.
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Example:
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>>> deepseek_model = DeepSeekModel()
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>>> response = deepseek_model.generate_response_dictionary([{"role": "user", "parts": "What is the weather in Tokyo?"}])
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"""
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try:
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response = self.client.messages.create( # Create a chat completion with the OpenAI API
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model=self.model_name, # Set the model type we want to use
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messages=prompt, # Set the prompt we want to use
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max_tokens=1024
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)
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return response.content[0].text if isinstance(response.content, list) else response.content
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except Exception as e:
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return f"Error generating response: {str(e)}"
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# -------------------------------------------------------------------
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# Helper to generate a response to a given prompt using a string
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# -------------------------------------------------------------------
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def generate_response_from_string(self, prompt, **kwargs):
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"""
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Description:
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Generate a response to a given prompt using a string.
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Args:
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prompt (str): The prompt to generate a response to.
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**kwargs: Additional keyword arguments.
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Returns:
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str: The generated response.
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Example:
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>>> deepseek_model = DeepSeekModel()
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>>> response = deepseek_model.generate_response_from_string("What is the weather in Tokyo?")
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"""
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# Extract personality and style from kwargs, or use defaults from agent_template
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if kwargs:
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if "personality" in kwargs:
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personality = kwargs.get("personality")
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if "communication_style" in kwargs:
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communication_style = kwargs.get("communication_style")
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else:
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personality = ""
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communication_style = ""
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try:
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# instructions being sent to the ai model
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messages = []
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# add personality and style to the instructions
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if personality or communication_style:
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persona_prompt = f"{personality} {communication_style}".strip()
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messages.append({
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"role": "system",
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"content": persona_prompt
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})
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# User message
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messages.append({
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"role": "user",
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"content": prompt
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})
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# generate the response
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# generate the response
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# response = self.client.messages.create( # Create a chat completion with the OpenAI API
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# model=self.model_name, # Set the model type we want to use
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# messages=messages, # Set the prompt we want to use
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# temperature=0.7, # Set the temperature of the response
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# max_tokens=400, # Set the maximum number of tokens to generate
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# )
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response = self.client.messages.create( # Create a chat completion with the OpenAI API
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model=self.model_name, # Set the model type we want to use
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messages=messages, # Set the prompt we want to use
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max_tokens=1024
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)
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return response.content[0].text if isinstance(response.content, list) else response.content
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except Exception as e:
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return f"Error generating response: {str(e)}"
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if __name__ == "__main__":
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deepseek_model = DeepSeekModel()
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response = deepseek_model.generate_response("Tell me 10 one liners about crypto. put them as a json object")
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# Extract just the text content from the response
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if hasattr(response, 'text'):
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print("\nClaude's Response:\n")
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print(response.text)
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print("\n")
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else:
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print("\nClaude's Response:\n")
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print(response)
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print("\n")
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