Source code for langchain_community.chat_models.everlyai

"""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 (

    import tiktoken

logger = logging.getLogger(__name__)

DEFAULT_MODEL = "meta-llama/Llama-2-7b-chat-hf"

[docs]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"}
[docs] @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."""
[docs] @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
[docs] def get_num_tokens_from_messages(self, messages: list[BaseMessage]) -> int: """Calculate num tokens with tiktoken package. Official documentation: 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