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"""EverlyAI Endpoints chat wrapper. Relies heavily on ChatOpenAI."""
from __future__ import annotations
import logging
import sys
from typing import TYPE_CHECKING, Dict, Optional, Set
from langchain_core.messages import BaseMessage
from langchain_core.pydantic_v1 import Field, root_validator
from langchain_core.utils import get_from_dict_or_env
from langchain_community.adapters.openai import convert_message_to_dict
from langchain_community.chat_models.openai import (
ChatOpenAI,
_import_tiktoken,
)
if TYPE_CHECKING:
import tiktoken
logger = logging.getLogger(__name__)
DEFAULT_API_BASE = "https://everlyai.xyz/hosted"
DEFAULT_MODEL = "meta-llama/Llama-2-7b-chat-hf"
class ChatEverlyAI(ChatOpenAI):
"""`EverlyAI` Chat large language models.
To use, you should have the ``openai`` python package installed, and the
environment variable ``EVERLYAI_API_KEY`` set with your API key.
Alternatively, you can use the everlyai_api_key keyword argument.
Any parameters that are valid to be passed to the `openai.create` call can be passed
in, even if not explicitly saved on this class.
Example:
.. code-block:: python
from langchain_community.chat_models import ChatEverlyAI
chat = ChatEverlyAI(model_name="meta-llama/Llama-2-7b-chat-hf")
"""
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
return "everlyai-chat"
@property
def lc_secrets(self) -> Dict[str, str]:
return {"everlyai_api_key": "EVERLYAI_API_KEY"}
@classmethod
def is_lc_serializable(cls) -> bool:
return False
everlyai_api_key: Optional[str] = None
"""EverlyAI Endpoints API keys."""
model_name: str = Field(default=DEFAULT_MODEL, alias="model")
"""Model name to use."""
everlyai_api_base: str = DEFAULT_API_BASE
"""Base URL path for API requests."""
available_models: Optional[Set[str]] = None
"""Available models from EverlyAI API."""
@staticmethod
def get_available_models() -> Set[str]:
"""Get available models from EverlyAI API."""
# EverlyAI doesn't yet support dynamically query for available models.
return set(
[
"meta-llama/Llama-2-7b-chat-hf",
"meta-llama/Llama-2-13b-chat-hf-quantized",
]
)
@root_validator(pre=True)
def validate_environment_override(cls, values: dict) -> dict:
"""Validate that api key and python package exists in environment."""
values["openai_api_key"] = get_from_dict_or_env(
values,
"everlyai_api_key",
"EVERLYAI_API_KEY",
)
values["openai_api_base"] = DEFAULT_API_BASE
try:
import openai
except ImportError as e:
raise ImportError(
"Could not import openai python package. "
"Please install it with `pip install openai`.",
) from e
try:
values["client"] = openai.ChatCompletion
except AttributeError as exc:
raise ValueError(
"`openai` has no `ChatCompletion` attribute, this is likely "
"due to an old version of the openai package. Try upgrading it "
"with `pip install --upgrade openai`.",
) from exc
if "model_name" not in values.keys():
values["model_name"] = DEFAULT_MODEL
model_name = values["model_name"]
available_models = cls.get_available_models()
if model_name not in available_models:
raise ValueError(
f"Model name {model_name} not found in available models: "
f"{available_models}.",
)
values["available_models"] = available_models
return values
def _get_encoding_model(self) -> tuple[str, tiktoken.Encoding]:
tiktoken_ = _import_tiktoken()
if self.tiktoken_model_name is not None:
model = self.tiktoken_model_name
else:
model = self.model_name
# Returns the number of tokens used by a list of messages.
try:
encoding = tiktoken_.encoding_for_model("gpt-3.5-turbo-0301")
except KeyError:
logger.warning("Warning: model not found. Using cl100k_base encoding.")
model = "cl100k_base"
encoding = tiktoken_.get_encoding(model)
return model, encoding
def get_num_tokens_from_messages(self, messages: list[BaseMessage]) -> int:
"""Calculate num tokens with tiktoken package.
Official documentation: https://github.com/openai/openai-cookbook/blob/
main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
if sys.version_info[1] <= 7:
return super().get_num_tokens_from_messages(messages)
model, encoding = self._get_encoding_model()
tokens_per_message = 3
tokens_per_name = 1
num_tokens = 0
messages_dict = [convert_message_to_dict(m) for m in messages]
for message in messages_dict:
num_tokens += tokens_per_message
for key, value in message.items():
# Cast str(value) in case the message value is not a string
# This occurs with function messages
num_tokens += len(encoding.encode(str(value)))
if key == "name":
num_tokens += tokens_per_name
# every reply is primed with <im_start>assistant
num_tokens += 3
return num_tokens
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