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

60 lines
2.7 KiB
Python

import logging
import os
from typing import Any, Optional
try:
from langchain_anthropic import ChatAnthropic
except ImportError:
raise ImportError("Please install the langchain-anthropic package by running `pip install langchain-anthropic`.")
from embedchain.config import BaseLlmConfig
from embedchain.helpers.json_serializable import register_deserializable
from embedchain.llm.base import BaseLlm
logger = logging.getLogger(__name__)
@register_deserializable
class AnthropicLlm(BaseLlm):
def __init__(self, config: Optional[BaseLlmConfig] = None):
super().__init__(config=config)
if not self.config.api_key and "ANTHROPIC_API_KEY" not in os.environ:
raise ValueError("Please set the ANTHROPIC_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 = "anthropic/" + 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) -> str:
api_key = config.api_key or os.getenv("ANTHROPIC_API_KEY")
chat = ChatAnthropic(anthropic_api_key=api_key, temperature=config.temperature, model_name=config.model)
if config.max_tokens and config.max_tokens != 1000:
logger.warning("Config option `max_tokens` is not supported by this model.")
messages = BaseLlm._get_messages(prompt, system_prompt=config.system_prompt)
chat_response = chat.invoke(messages)
if config.token_usage:
return chat_response.content, chat_response.response_metadata["token_usage"]
return chat_response.content