mirror of
https://github.com/hwchase17/langchain.git
synced 2025-05-02 05:45:47 +00:00
a0c2281540
```python
"""python scripts/update_mypy_ruff.py"""
import glob
import tomllib
from pathlib import Path
import toml
import subprocess
import re
ROOT_DIR = Path(__file__).parents[1]
def main():
for path in glob.glob(str(ROOT_DIR / "libs/**/pyproject.toml"), recursive=True):
print(path)
with open(path, "rb") as f:
pyproject = tomllib.load(f)
try:
pyproject["tool"]["poetry"]["group"]["typing"]["dependencies"]["mypy"] = (
"^1.10"
)
pyproject["tool"]["poetry"]["group"]["lint"]["dependencies"]["ruff"] = (
"^0.5"
)
except KeyError:
continue
with open(path, "w") as f:
toml.dump(pyproject, f)
cwd = "/".join(path.split("/")[:-1])
completed = subprocess.run(
"poetry lock --no-update; poetry install --with typing; poetry run mypy . --no-color",
cwd=cwd,
shell=True,
capture_output=True,
text=True,
)
logs = completed.stdout.split("\n")
to_ignore = {}
for l in logs:
if re.match("^(.*)\:(\d+)\: error:.*\[(.*)\]", l):
path, line_no, error_type = re.match(
"^(.*)\:(\d+)\: error:.*\[(.*)\]", l
).groups()
if (path, line_no) in to_ignore:
to_ignore[(path, line_no)].append(error_type)
else:
to_ignore[(path, line_no)] = [error_type]
print(len(to_ignore))
for (error_path, line_no), error_types in to_ignore.items():
all_errors = ", ".join(error_types)
full_path = f"{cwd}/{error_path}"
try:
with open(full_path, "r") as f:
file_lines = f.readlines()
except FileNotFoundError:
continue
file_lines[int(line_no) - 1] = (
file_lines[int(line_no) - 1][:-1] + f" # type: ignore[{all_errors}]\n"
)
with open(full_path, "w") as f:
f.write("".join(file_lines))
subprocess.run(
"poetry run ruff format .; poetry run ruff --select I --fix .",
cwd=cwd,
shell=True,
capture_output=True,
text=True,
)
if __name__ == "__main__":
main()
```
284 lines
10 KiB
Python
284 lines
10 KiB
Python
"""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
|
|
|
|
|
|
class ChatYandexGPT(_BaseYandexGPT, BaseChatModel):
|
|
"""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
|
|
|
|
try:
|
|
from yandex.cloud.ai.foundation_models.v1.text_common_pb2 import (
|
|
CompletionOptions,
|
|
Message,
|
|
)
|
|
from yandex.cloud.ai.foundation_models.v1.text_generation.text_generation_service_pb2 import ( # noqa: E501
|
|
CompletionRequest,
|
|
)
|
|
from yandex.cloud.ai.foundation_models.v1.text_generation.text_generation_service_pb2_grpc import ( # noqa: E501
|
|
TextGenerationServiceStub,
|
|
)
|
|
except ModuleNotFoundError:
|
|
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
|
|
|
|
try:
|
|
from yandex.cloud.ai.foundation_models.v1.text_common_pb2 import (
|
|
CompletionOptions,
|
|
Message,
|
|
)
|
|
from yandex.cloud.ai.foundation_models.v1.text_generation.text_generation_service_pb2 import ( # noqa: E501
|
|
CompletionRequest,
|
|
CompletionResponse,
|
|
)
|
|
from yandex.cloud.ai.foundation_models.v1.text_generation.text_generation_service_pb2_grpc import ( # noqa: E501
|
|
TextGenerationAsyncServiceStub,
|
|
)
|
|
except ModuleNotFoundError:
|
|
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),
|
|
)
|
|
|
|
|
|
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)
|
|
|
|
|
|
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)
|