69 lines
3.0 KiB
Python
69 lines
3.0 KiB
Python
import importlib
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import logging
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from typing import Any, Optional
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from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
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from langchain_google_vertexai import ChatVertexAI
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from embedchain.config import BaseLlmConfig
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from embedchain.helpers.json_serializable import register_deserializable
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from embedchain.llm.base import BaseLlm
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logger = logging.getLogger(__name__)
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@register_deserializable
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class VertexAILlm(BaseLlm):
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def __init__(self, config: Optional[BaseLlmConfig] = None):
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try:
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importlib.import_module("vertexai")
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except ModuleNotFoundError:
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raise ModuleNotFoundError(
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"The required dependencies for VertexAI are not installed."
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'Please install with `pip install --upgrade "embedchain[vertexai]"`'
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) from None
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super().__init__(config=config)
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def get_llm_model_answer(self, prompt) -> tuple[str, Optional[dict[str, Any]]]:
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if self.config.token_usage:
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response, token_info = self._get_answer(prompt, self.config)
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model_name = "vertexai/" + self.config.model
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if model_name not in self.config.model_pricing_map:
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raise ValueError(
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f"Model {model_name} not found in `model_prices_and_context_window.json`. \
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You can disable token usage by setting `token_usage` to False."
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)
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total_cost = (
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self.config.model_pricing_map[model_name]["input_cost_per_token"] * token_info["prompt_token_count"]
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) + self.config.model_pricing_map[model_name]["output_cost_per_token"] * token_info[
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"candidates_token_count"
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]
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response_token_info = {
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"prompt_tokens": token_info["prompt_token_count"],
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"completion_tokens": token_info["candidates_token_count"],
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"total_tokens": token_info["prompt_token_count"] + token_info["candidates_token_count"],
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"total_cost": round(total_cost, 10),
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"cost_currency": "USD",
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}
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return response, response_token_info
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return self._get_answer(prompt, self.config)
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@staticmethod
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def _get_answer(prompt: str, config: BaseLlmConfig) -> str:
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if config.top_p and config.top_p != 1:
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logger.warning("Config option `top_p` is not supported by this model.")
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if config.stream:
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callbacks = config.callbacks if config.callbacks else [StreamingStdOutCallbackHandler()]
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llm = ChatVertexAI(
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temperature=config.temperature, model=config.model, callbacks=callbacks, streaming=config.stream
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)
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else:
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llm = ChatVertexAI(temperature=config.temperature, model=config.model)
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messages = VertexAILlm._get_messages(prompt)
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chat_response = llm.invoke(messages)
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if config.token_usage:
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return chat_response.content, chat_response.response_metadata["usage_metadata"]
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return chat_response.content
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