import asyncio
from functools import partial
from typing import (
Any,
Callable,
Dict,
Iterator,
List,
Mapping,
Optional,
Sequence,
Type,
Union,
)
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models import LanguageModelInput
from langchain_core.language_models.chat_models import (
BaseChatModel,
LangSmithParams,
generate_from_stream,
)
from langchain_core.messages import (
BaseMessage,
)
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.pydantic_v1 import root_validator
from langchain_core.runnables import Runnable
from langchain_core.tools import BaseTool
from langchain_core.utils.function_calling import convert_to_openai_tool
from langchain_ai21.ai21_base import AI21Base
from langchain_ai21.chat.chat_adapter import ChatAdapter
from langchain_ai21.chat.chat_factory import create_chat_adapter
[docs]class ChatAI21(BaseChatModel, AI21Base):
"""ChatAI21 chat model. Different model types support different parameters and
different parameter values. Please read the [AI21 reference documentation]
(https://docs.ai21.com/reference) for your model to understand which parameters
are available.
Example:
.. code-block:: python
from langchain_ai21 import ChatAI21
model = ChatAI21(
# defaults to os.environ.get("AI21_API_KEY")
api_key="my_api_key"
)
"""
model: str
"""Model type you wish to interact with.
You can view the options at https://github.com/AI21Labs/ai21-python?tab=readme-ov-file#model-types"""
num_results: int = 1
"""The number of responses to generate for a given prompt."""
stop: Optional[List[str]] = None
"""Default stop sequences."""
max_tokens: int = 512
"""The maximum number of tokens to generate for each response."""
min_tokens: int = 0
"""The minimum number of tokens to generate for each response.
_Not supported for all models._"""
temperature: float = 0.4
"""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.
_Not supported for all models._"""
frequency_penalty: Optional[Any] = None
"""A penalty applied to tokens that are frequently generated.
_Not supported for all models._"""
presence_penalty: Optional[Any] = None
""" A penalty applied to tokens that are already present in the prompt.
_Not supported for all models._"""
count_penalty: Optional[Any] = None
"""A penalty applied to tokens based on their frequency
in the generated responses. _Not supported for all models._"""
n: int = 1
"""Number of chat completions to generate for each prompt."""
streaming: bool = False
_chat_adapter: ChatAdapter
@root_validator(pre=False, skip_on_failure=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate the environment."""
model = values["model"]
values["_chat_adapter"] = create_chat_adapter(model)
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.stop:
base_params["stop_sequences"] = self.stop
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 _get_ls_params(
self, stop: Optional[List[str]] = None, **kwargs: Any
) -> LangSmithParams:
"""Get standard params for tracing."""
params = self._get_invocation_params(stop=stop, **kwargs)
ls_params = LangSmithParams(
ls_provider="ai21",
ls_model_name=self.model,
ls_model_type="chat",
ls_temperature=params.get("temperature", self.temperature),
)
if ls_max_tokens := params.get("max_tokens", self.max_tokens):
ls_params["ls_max_tokens"] = ls_max_tokens
if ls_stop := stop or params.get("stop", None) or self.stop:
ls_params["ls_stop"] = ls_stop
return ls_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,
stream: Optional[bool] = None,
**kwargs: Any,
) -> ChatResult:
should_stream = stream or self.streaming
if should_stream:
return self._handle_stream_from_generate(
messages=messages,
stop=stop,
run_manager=run_manager,
**kwargs,
)
params = self._build_params_for_request(
messages=messages,
stop=stop,
stream=should_stream,
**kwargs,
)
messages = self._chat_adapter.call(self.client, **params)
generations = [ChatGeneration(message=message) for message in messages]
return ChatResult(generations=generations)
def _handle_stream_from_generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
stream_iter = self._stream(
messages=messages,
stop=stop,
run_manager=run_manager,
**kwargs,
)
return generate_from_stream(stream_iter)
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
params = self._build_params_for_request(
messages=messages,
stop=stop,
stream=True,
**kwargs,
)
for chunk in self._chat_adapter.call(self.client, **params):
if run_manager and isinstance(chunk.message.content, str):
run_manager.on_llm_new_token(token=chunk.message.content, chunk=chunk)
yield chunk
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
