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import os |
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from langchain_openai import ChatOpenAI |
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from langchain_community.embeddings import HuggingFaceEmbeddings |
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from langchain_together import Together |
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from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings |
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from dotenv import load_dotenv |
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load_dotenv() |
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class PersonalModels: |
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def get_chat_model(self, |
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model: str = 'mistralai/Mistral-7B-Instruct-v0.2', |
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api_key: str = os.environ.get('TOGETHER_API_KEY', ''), |
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max_tokens: int = 1000, |
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temperature: float = 0.0, |
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verbose: bool = True) -> ChatOpenAI: |
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llm = ChatOpenAI( |
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api_key=api_key, |
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base_url="https://api.together.xyz/v1", |
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model=model, |
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max_tokens=max_tokens, |
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temperature=temperature, |
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verbose=verbose |
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) |
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return llm |
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def get_embedding_model(self, |
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model: str = 'intfloat/multilingual-e5-large') -> HuggingFaceInferenceAPIEmbeddings: |
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embeddings = HuggingFaceInferenceAPIEmbeddings(api_key=os.environ.get('HUGGINGFACEHUB_API_TOKEN', ''), model_name=model) |
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return embeddings |
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def get_rag_model(self, |
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model: str = 'mistralai/Mistral-7B-Instruct-v0.2', |
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api_key: str = os.environ.get('TOGETHER_API_KEY', ''), |
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max_tokens: int = 1000, |
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temperature: float = 0.0, |
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top_k: int = 20, |
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verbose: bool = True) -> Together: |
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llm = Together( |
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model=model, |
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temperature=temperature, |
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max_tokens=max_tokens, |
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top_k=top_k, |
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together_api_key=api_key, |
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verbose=verbose |
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) |
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return llm |
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