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75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
from typing import Optional
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from langchain.embeddings.openai import OpenAIEmbeddings
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from langchain.llms.base import BaseLLM
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from langchain.llms.gpt4all import GPT4All
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from llm.qa_base import QABaseBrainPicking
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from logger import get_logger
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logger = get_logger(__name__)
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class PrivateGPT4AllBrainPicking(QABaseBrainPicking):
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"""
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This subclass of BrainPicking is used to specifically work with the private language model GPT4All.
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"""
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# Define the default model path
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model_path: str = "./local_models/ggml-gpt4all-j-v1.3-groovy.bin"
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def __init__(
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self,
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chat_id: str,
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brain_id: str,
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user_openai_api_key: Optional[str],
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streaming: bool,
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model_path: str,
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) -> None:
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"""
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Initialize the PrivateBrainPicking class by calling the parent class's initializer.
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:param brain_id: The brain_id in the DB.
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:param chat_id: The id of the chat in the DB.
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:param streaming: Whether to enable streaming of the model
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:param model_path: The path to the model. If not provided, a default path is used.
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"""
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super().__init__(
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model="gpt4all-j-1.3",
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brain_id=brain_id,
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chat_id=chat_id,
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user_openai_api_key=user_openai_api_key,
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streaming=streaming,
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)
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# Set the model path
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self.model_path = model_path
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# TODO: Use private embeddings model. This involves some restructuring of how we store the embeddings.
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@property
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def embeddings(self) -> OpenAIEmbeddings:
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return OpenAIEmbeddings(
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openai_api_key=self.openai_api_key
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) # pyright: ignore reportPrivateUsage=none
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def _create_llm(
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self,
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model,
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streaming=False,
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callbacks=None,
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) -> BaseLLM:
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"""
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Override the _create_llm method to enforce the use of a private model.
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:param model: Language model name to be used.
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:param streaming: Whether to enable streaming of the model
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:param callbacks: Callbacks to be used for streaming
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:return: Language model instance
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"""
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model_path = self.model_path
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logger.info("Using private model: %s", model)
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logger.info("Streaming is set to %s", streaming)
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return GPT4All(
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model=model_path,
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) # pyright: ignore reportPrivateUsage=none
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