Source code for langchain_ai21.chat_models

import asyncio
from functools import partial
from typing import Any, Dict, List, Mapping, Optional

from langchain_core.callbacks import (
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.messages import (
from langchain_core.outputs import ChatGeneration, ChatResult
from langchain_core.pydantic_v1 import root_validator

from langchain_ai21.ai21_base import AI21Base
from import ChatAdapter
from import create_chat_adapter

[docs]class ChatAI21(BaseChatModel, AI21Base): """ChatAI21 chat model. Example: .. code-block:: python from langchain_ai21 import ChatAI21 model = ChatAI21() """ model: str """Model type you wish to interact with. You can view the options at""" num_results: int = 1 """The number of responses to generate for a given prompt.""" max_tokens: int = 16 """The maximum number of tokens to generate for each response.""" min_tokens: int = 0 """The minimum number of tokens to generate for each response.""" temperature: float = 0.7 """A value controlling the "creativity" of the model's responses.""" top_p: float = 1 """A value controlling the diversity of the model's responses.""" top_k_return: int = 0 """The number of top-scoring tokens to consider for each generation step.""" frequency_penalty: Optional[Any] = None """A penalty applied to tokens that are frequently generated.""" presence_penalty: Optional[Any] = None """ A penalty applied to tokens that are already present in the prompt.""" count_penalty: Optional[Any] = None """A penalty applied to tokens based on their frequency in the generated responses.""" n: int = 1 """Number of chat completions to generate for each prompt.""" _chat_adapter: ChatAdapter @root_validator() def validate_environment(cls, values: Dict) -> Dict: values = super().validate_environment(values) model = values.get("model") values["_chat_adapter"] = create_chat_adapter(model) # type: ignore return values class Config: """Configuration for this pydantic object.""" arbitrary_types_allowed = True @property def _llm_type(self) -> str: """Return type of chat model.""" return "chat-ai21" @property def _default_params(self) -> Mapping[str, Any]: base_params = { "model": self.model, "num_results": self.num_results, "max_tokens": self.max_tokens, "min_tokens": self.min_tokens, "temperature": self.temperature, "top_p": self.top_p, "top_k_return": self.top_k_return, "n": self.n, } if self.count_penalty is not None: base_params["count_penalty"] = self.count_penalty.to_dict() if self.frequency_penalty is not None: base_params["frequency_penalty"] = self.frequency_penalty.to_dict() if self.presence_penalty is not None: base_params["presence_penalty"] = self.presence_penalty.to_dict() return base_params def _build_params_for_request( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, **kwargs: Any, ) -> Mapping[str, Any]: params = {} converted_messages = self._chat_adapter.convert_messages(messages) if stop is not None: if "stop" in kwargs: raise ValueError("stop is defined in both stop and kwargs") params["stop_sequences"] = stop return { **converted_messages, **self._default_params, **params, **kwargs, } def _generate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> ChatResult: params = self._build_params_for_request(messages=messages, stop=stop, **kwargs) messages =, **params) generations = [ChatGeneration(message=message) for message in messages] return ChatResult(generations=generations) async def _agenerate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> ChatResult: return await asyncio.get_running_loop().run_in_executor( None, partial(self._generate, **kwargs), messages, stop, run_manager ) def _get_system_message_from_message(self, message: BaseMessage) -> str: if not isinstance(message.content, str): raise ValueError( f"System Message must be of type str. Got {type(message.content)}" ) return message.content