Files
t6_mem0/embedchain/llm/nvidia.py
2024-07-04 11:40:56 -07:00

69 lines
3.3 KiB
Python

import os
from collections.abc import Iterable
from typing import Any, Optional, Union
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.stdout import StdOutCallbackHandler
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
try:
from langchain_nvidia_ai_endpoints import ChatNVIDIA
except ImportError:
raise ImportError(
"NVIDIA AI endpoints requires extra dependencies. Install with `pip install langchain-nvidia-ai-endpoints`"
) from None
from embedchain.config import BaseLlmConfig
from embedchain.helpers.json_serializable import register_deserializable
from embedchain.llm.base import BaseLlm
@register_deserializable
class NvidiaLlm(BaseLlm):
def __init__(self, config: Optional[BaseLlmConfig] = None):
super().__init__(config=config)
if not self.config.api_key and "NVIDIA_API_KEY" not in os.environ:
raise ValueError("Please set the NVIDIA_API_KEY environment variable or pass it in the config.")
def get_llm_model_answer(self, prompt) -> tuple[str, Optional[dict[str, Any]]]:
if self.config.token_usage:
response, token_info = self._get_answer(prompt, self.config)
model_name = "nvidia/" + self.config.model
if model_name not in self.config.model_pricing_map:
raise ValueError(
f"Model {model_name} not found in `model_prices_and_context_window.json`. \
You can disable token usage by setting `token_usage` to False."
)
total_cost = (
self.config.model_pricing_map[model_name]["input_cost_per_token"] * token_info["input_tokens"]
) + self.config.model_pricing_map[model_name]["output_cost_per_token"] * token_info["output_tokens"]
response_token_info = {
"prompt_tokens": token_info["input_tokens"],
"completion_tokens": token_info["output_tokens"],
"total_tokens": token_info["input_tokens"] + token_info["output_tokens"],
"total_cost": round(total_cost, 10),
"cost_currency": "USD",
}
return response, response_token_info
return self._get_answer(prompt, self.config)
@staticmethod
def _get_answer(prompt: str, config: BaseLlmConfig) -> Union[str, Iterable]:
callback_manager = [StreamingStdOutCallbackHandler()] if config.stream else [StdOutCallbackHandler()]
model_kwargs = config.model_kwargs or {}
labels = model_kwargs.get("labels", None)
params = {"model": config.model, "nvidia_api_key": config.api_key or os.getenv("NVIDIA_API_KEY")}
if config.system_prompt:
params["system_prompt"] = config.system_prompt
if config.temperature:
params["temperature"] = config.temperature
if config.top_p:
params["top_p"] = config.top_p
if labels:
params["labels"] = labels
llm = ChatNVIDIA(**params, callback_manager=CallbackManager(callback_manager))
chat_response = llm.invoke(prompt) if labels is None else llm.invoke(prompt, labels=labels)
if config.token_usage:
return chat_response.content, chat_response.response_metadata["token_usage"]
return chat_response.content