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40 lines
1.6 KiB
Python
40 lines
1.6 KiB
Python
from application.llm.base import BaseLLM
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from application.core.settings import settings
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class AnthropicLLM(BaseLLM):
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def __init__(self, api_key=None):
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from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT
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self.api_key = api_key or settings.ANTHROPIC_API_KEY # If not provided, use a default from settings
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self.anthropic = Anthropic(api_key=self.api_key)
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self.HUMAN_PROMPT = HUMAN_PROMPT
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self.AI_PROMPT = AI_PROMPT
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def gen(self, model, messages, engine=None, max_tokens=300, stream=False, **kwargs):
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context = messages[0]['content']
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user_question = messages[-1]['content']
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prompt = f"### Context \n {context} \n ### Question \n {user_question}"
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if stream:
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return self.gen_stream(model, prompt, max_tokens, **kwargs)
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completion = self.anthropic.completions.create(
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model=model,
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max_tokens_to_sample=max_tokens,
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stream=stream,
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prompt=f"{self.HUMAN_PROMPT} {prompt}{self.AI_PROMPT}",
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)
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return completion.completion
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def gen_stream(self, model, messages, engine=None, max_tokens=300, **kwargs):
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context = messages[0]['content']
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user_question = messages[-1]['content']
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prompt = f"### Context \n {context} \n ### Question \n {user_question}"
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stream_response = self.anthropic.completions.create(
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model=model,
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prompt=f"{self.HUMAN_PROMPT} {prompt}{self.AI_PROMPT}",
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max_tokens_to_sample=max_tokens,
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stream=True,
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)
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for completion in stream_response:
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yield completion.completion |