mirror of
https://github.com/hwchase17/langchain
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256 lines
9.3 KiB
Python
256 lines
9.3 KiB
Python
"""Wrapper around YandexGPT chat models."""
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from __future__ import annotations
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import logging
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from typing import Any, Callable, Dict, List, Optional, cast
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from langchain_core.callbacks import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models.chat_models import BaseChatModel
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from langchain_core.messages import (
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AIMessage,
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BaseMessage,
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HumanMessage,
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SystemMessage,
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)
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from langchain_core.outputs import ChatGeneration, ChatResult
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from tenacity import (
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before_sleep_log,
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retry,
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retry_if_exception_type,
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stop_after_attempt,
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wait_exponential,
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)
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from langchain_community.llms.utils import enforce_stop_tokens
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from langchain_community.llms.yandex import _BaseYandexGPT
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logger = logging.getLogger(__name__)
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def _parse_message(role: str, text: str) -> Dict:
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return {"role": role, "text": text}
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def _parse_chat_history(history: List[BaseMessage]) -> List[Dict[str, str]]:
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"""Parse a sequence of messages into history.
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Returns:
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A list of parsed messages.
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"""
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chat_history = []
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for message in history:
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content = cast(str, message.content)
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if isinstance(message, HumanMessage):
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chat_history.append(_parse_message("user", content))
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if isinstance(message, AIMessage):
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chat_history.append(_parse_message("assistant", content))
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if isinstance(message, SystemMessage):
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chat_history.append(_parse_message("system", content))
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return chat_history
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class ChatYandexGPT(_BaseYandexGPT, BaseChatModel):
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"""Wrapper around YandexGPT large language models.
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There are two authentication options for the service account
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with the ``ai.languageModels.user`` role:
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- You can specify the token in a constructor parameter `iam_token`
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or in an environment variable `YC_IAM_TOKEN`.
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- You can specify the key in a constructor parameter `api_key`
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or in an environment variable `YC_API_KEY`.
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Example:
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.. code-block:: python
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from langchain_community.chat_models import ChatYandexGPT
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chat_model = ChatYandexGPT(iam_token="t1.9eu...")
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"""
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def _generate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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"""Generate next turn in the conversation.
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Args:
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messages: The history of the conversation as a list of messages.
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stop: The list of stop words (optional).
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run_manager: The CallbackManager for LLM run, it's not used at the moment.
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Returns:
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The ChatResult that contains outputs generated by the model.
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Raises:
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ValueError: if the last message in the list is not from human.
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"""
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text = completion_with_retry(self, messages=messages)
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text = text if stop is None else enforce_stop_tokens(text, stop)
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message = AIMessage(content=text)
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return ChatResult(generations=[ChatGeneration(message=message)])
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async def _agenerate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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"""Async method to generate next turn in the conversation.
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Args:
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messages: The history of the conversation as a list of messages.
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stop: The list of stop words (optional).
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run_manager: The CallbackManager for LLM run, it's not used at the moment.
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Returns:
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The ChatResult that contains outputs generated by the model.
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Raises:
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ValueError: if the last message in the list is not from human.
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"""
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text = await acompletion_with_retry(self, messages=messages)
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text = text if stop is None else enforce_stop_tokens(text, stop)
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message = AIMessage(content=text)
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return ChatResult(generations=[ChatGeneration(message=message)])
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def _make_request(
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self: ChatYandexGPT,
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messages: List[BaseMessage],
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) -> str:
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try:
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import grpc
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from google.protobuf.wrappers_pb2 import DoubleValue, Int64Value
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from yandex.cloud.ai.foundation_models.v1.foundation_models_pb2 import (
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CompletionOptions,
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Message,
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)
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from yandex.cloud.ai.foundation_models.v1.foundation_models_service_pb2 import ( # noqa: E501
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CompletionRequest,
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)
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from yandex.cloud.ai.foundation_models.v1.foundation_models_service_pb2_grpc import ( # noqa: E501
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TextGenerationServiceStub,
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)
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except ImportError as e:
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raise ImportError(
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"Please install YandexCloud SDK with `pip install yandexcloud` \
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or upgrade it to recent version."
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) from e
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if not messages:
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raise ValueError("You should provide at least one message to start the chat!")
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message_history = _parse_chat_history(messages)
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channel_credentials = grpc.ssl_channel_credentials()
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channel = grpc.secure_channel(self.url, channel_credentials)
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request = CompletionRequest(
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model_uri=self.model_uri,
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completion_options=CompletionOptions(
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temperature=DoubleValue(value=self.temperature),
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max_tokens=Int64Value(value=self.max_tokens),
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),
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messages=[Message(**message) for message in message_history],
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)
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stub = TextGenerationServiceStub(channel)
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res = stub.Completion(request, metadata=self._grpc_metadata)
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return list(res)[0].alternatives[0].message.text
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async def _amake_request(self: ChatYandexGPT, messages: List[BaseMessage]) -> str:
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try:
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import asyncio
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import grpc
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from google.protobuf.wrappers_pb2 import DoubleValue, Int64Value
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from yandex.cloud.ai.foundation_models.v1.foundation_models_pb2 import (
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CompletionOptions,
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Message,
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)
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from yandex.cloud.ai.foundation_models.v1.foundation_models_service_pb2 import ( # noqa: E501
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CompletionRequest,
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CompletionResponse,
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)
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from yandex.cloud.ai.foundation_models.v1.foundation_models_service_pb2_grpc import ( # noqa: E501
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TextGenerationAsyncServiceStub,
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)
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from yandex.cloud.operation.operation_service_pb2 import GetOperationRequest
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from yandex.cloud.operation.operation_service_pb2_grpc import (
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OperationServiceStub,
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)
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except ImportError as e:
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raise ImportError(
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"Please install YandexCloud SDK with `pip install yandexcloud` \
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or upgrade it to recent version."
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) from e
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if not messages:
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raise ValueError("You should provide at least one message to start the chat!")
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message_history = _parse_chat_history(messages)
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operation_api_url = "operation.api.cloud.yandex.net:443"
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channel_credentials = grpc.ssl_channel_credentials()
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async with grpc.aio.secure_channel(self.url, channel_credentials) as channel:
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request = CompletionRequest(
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model_uri=self.model_uri,
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completion_options=CompletionOptions(
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temperature=DoubleValue(value=self.temperature),
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max_tokens=Int64Value(value=self.max_tokens),
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),
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messages=[Message(**message) for message in message_history],
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)
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stub = TextGenerationAsyncServiceStub(channel)
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operation = await stub.Completion(request, metadata=self._grpc_metadata)
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async with grpc.aio.secure_channel(
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operation_api_url, channel_credentials
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) as operation_channel:
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operation_stub = OperationServiceStub(operation_channel)
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while not operation.done:
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await asyncio.sleep(1)
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operation_request = GetOperationRequest(operation_id=operation.id)
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operation = await operation_stub.Get(
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operation_request,
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metadata=self._grpc_metadata,
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)
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completion_response = CompletionResponse()
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operation.response.Unpack(completion_response)
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return completion_response.alternatives[0].message.text
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def _create_retry_decorator(llm: ChatYandexGPT) -> Callable[[Any], Any]:
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from grpc import RpcError
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min_seconds = llm.sleep_interval
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max_seconds = 60
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return retry(
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reraise=True,
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stop=stop_after_attempt(llm.max_retries),
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wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
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retry=(retry_if_exception_type((RpcError))),
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before_sleep=before_sleep_log(logger, logging.WARNING),
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)
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def completion_with_retry(llm: ChatYandexGPT, **kwargs: Any) -> Any:
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"""Use tenacity to retry the completion call."""
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retry_decorator = _create_retry_decorator(llm)
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@retry_decorator
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def _completion_with_retry(**_kwargs: Any) -> Any:
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return _make_request(llm, **_kwargs)
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return _completion_with_retry(**kwargs)
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async def acompletion_with_retry(llm: ChatYandexGPT, **kwargs: Any) -> Any:
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"""Use tenacity to retry the async completion call."""
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retry_decorator = _create_retry_decorator(llm)
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@retry_decorator
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async def _completion_with_retry(**_kwargs: Any) -> Any:
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return await _amake_request(llm, **_kwargs)
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return await _completion_with_retry(**kwargs)
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