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