"""Wrapper around YandexGPT chat models."""
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional, cast
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.messages import (
AIMessage,
BaseMessage,
HumanMessage,
SystemMessage,
)
from langchain_core.outputs import ChatGeneration, ChatResult
from tenacity import (
before_sleep_log,
retry,
retry_if_exception_type,
stop_after_attempt,
wait_exponential,
)
from langchain_community.llms.utils import enforce_stop_tokens
from langchain_community.llms.yandex import _BaseYandexGPT
logger = logging.getLogger(__name__)
def _parse_message(role: str, text: str) -> Dict:
return {"role": role, "text": text}
def _parse_chat_history(history: List[BaseMessage]) -> List[Dict[str, str]]:
"""Parse a sequence of messages into history.
Returns:
A list of parsed messages.
"""
chat_history = []
for message in history:
content = cast(str, message.content)
if isinstance(message, HumanMessage):
chat_history.append(_parse_message("user", content))
if isinstance(message, AIMessage):
chat_history.append(_parse_message("assistant", content))
if isinstance(message, SystemMessage):
chat_history.append(_parse_message("system", content))
return chat_history
[docs]class ChatYandexGPT(_BaseYandexGPT, BaseChatModel):
"""Wrapper around YandexGPT large language models.
There are two authentication options for the service account
with the ``ai.languageModels.user`` role:
- You can specify the token in a constructor parameter `iam_token`
or in an environment variable `YC_IAM_TOKEN`.
- You can specify the key in a constructor parameter `api_key`
or in an environment variable `YC_API_KEY`.
Example:
.. code-block:: python
from langchain_community.chat_models import ChatYandexGPT
chat_model = ChatYandexGPT(iam_token="t1.9eu...")
"""
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
"""Generate next turn in the conversation.
Args:
messages: The history of the conversation as a list of messages.
stop: The list of stop words (optional).
run_manager: The CallbackManager for LLM run, it's not used at the moment.
Returns:
The ChatResult that contains outputs generated by the model.
Raises:
ValueError: if the last message in the list is not from human.
"""
text = completion_with_retry(self, messages=messages)
text = text if stop is None else enforce_stop_tokens(text, stop)
message = AIMessage(content=text)
return ChatResult(generations=[ChatGeneration(message=message)])
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
"""Async method to generate next turn in the conversation.
Args:
messages: The history of the conversation as a list of messages.
stop: The list of stop words (optional).
run_manager: The CallbackManager for LLM run, it's not used at the moment.
Returns:
The ChatResult that contains outputs generated by the model.
Raises:
ValueError: if the last message in the list is not from human.
"""
text = await acompletion_with_retry(self, messages=messages)
text = text if stop is None else enforce_stop_tokens(text, stop)
message = AIMessage(content=text)
return ChatResult(generations=[ChatGeneration(message=message)])
def _make_request(
self: ChatYandexGPT,
messages: List[BaseMessage],
) -> str:
try:
import grpc
from google.protobuf.wrappers_pb2 import DoubleValue, Int64Value
from yandex.cloud.ai.foundation_models.v1.foundation_models_pb2 import (
CompletionOptions,
Message,
)
from yandex.cloud.ai.foundation_models.v1.foundation_models_service_pb2 import ( # noqa: E501
CompletionRequest,
)
from yandex.cloud.ai.foundation_models.v1.foundation_models_service_pb2_grpc import ( # noqa: E501
TextGenerationServiceStub,
)
except ImportError as e:
raise ImportError(
"Please install YandexCloud SDK with `pip install yandexcloud` \
or upgrade it to recent version."
) from e
if not messages:
raise ValueError("You should provide at least one message to start the chat!")
message_history = _parse_chat_history(messages)
channel_credentials = grpc.ssl_channel_credentials()
channel = grpc.secure_channel(self.url, channel_credentials)
request = CompletionRequest(
model_uri=self.model_uri,
completion_options=CompletionOptions(
temperature=DoubleValue(value=self.temperature),
max_tokens=Int64Value(value=self.max_tokens),
),
messages=[Message(**message) for message in message_history],
)
stub = TextGenerationServiceStub(channel)
res = stub.Completion(request, metadata=self._grpc_metadata)
return list(res)[0].alternatives[0].message.text
async def _amake_request(self: ChatYandexGPT, messages: List[BaseMessage]) -> str:
try:
import asyncio
import grpc
from google.protobuf.wrappers_pb2 import DoubleValue, Int64Value
from yandex.cloud.ai.foundation_models.v1.foundation_models_pb2 import (
CompletionOptions,
Message,
)
from yandex.cloud.ai.foundation_models.v1.foundation_models_service_pb2 import ( # noqa: E501
CompletionRequest,
CompletionResponse,
)
from yandex.cloud.ai.foundation_models.v1.foundation_models_service_pb2_grpc import ( # noqa: E501
TextGenerationAsyncServiceStub,
)
from yandex.cloud.operation.operation_service_pb2 import GetOperationRequest
from yandex.cloud.operation.operation_service_pb2_grpc import (
OperationServiceStub,
)
except ImportError as e:
raise ImportError(
"Please install YandexCloud SDK with `pip install yandexcloud` \
or upgrade it to recent version."
) from e
if not messages:
raise ValueError("You should provide at least one message to start the chat!")
message_history = _parse_chat_history(messages)
operation_api_url = "operation.api.cloud.yandex.net:443"
channel_credentials = grpc.ssl_channel_credentials()
async with grpc.aio.secure_channel(self.url, channel_credentials) as channel:
request = CompletionRequest(
model_uri=self.model_uri,
completion_options=CompletionOptions(
temperature=DoubleValue(value=self.temperature),
max_tokens=Int64Value(value=self.max_tokens),
),
messages=[Message(**message) for message in message_history],
)
stub = TextGenerationAsyncServiceStub(channel)
operation = await stub.Completion(request, metadata=self._grpc_metadata)
async with grpc.aio.secure_channel(
operation_api_url, channel_credentials
) as operation_channel:
operation_stub = OperationServiceStub(operation_channel)
while not operation.done:
await asyncio.sleep(1)
operation_request = GetOperationRequest(operation_id=operation.id)
operation = await operation_stub.Get(
operation_request,
metadata=self._grpc_metadata,
)
completion_response = CompletionResponse()
operation.response.Unpack(completion_response)
return completion_response.alternatives[0].message.text
def _create_retry_decorator(llm: ChatYandexGPT) -> Callable[[Any], Any]:
from grpc import RpcError
min_seconds = llm.sleep_interval
max_seconds = 60
return retry(
reraise=True,
stop=stop_after_attempt(llm.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=(retry_if_exception_type((RpcError))),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
[docs]def completion_with_retry(llm: ChatYandexGPT, **kwargs: Any) -> Any:
"""Use tenacity to retry the completion call."""
retry_decorator = _create_retry_decorator(llm)
@retry_decorator
def _completion_with_retry(**_kwargs: Any) -> Any:
return _make_request(llm, **_kwargs)
return _completion_with_retry(**kwargs)
[docs]async def acompletion_with_retry(llm: ChatYandexGPT, **kwargs: Any) -> Any:
"""Use tenacity to retry the async completion call."""
retry_decorator = _create_retry_decorator(llm)
@retry_decorator
async def _completion_with_retry(**_kwargs: Any) -> Any:
return await _amake_request(llm, **_kwargs)
return await _completion_with_retry(**kwargs)