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proxy_llm.py
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proxy_llm.py
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"""proxy llm chat wrapper."""
from __future__ import annotations
import json
import logging
import sys
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Mapping, Optional, Tuple, Union
from bisheng_langchain.utils import requests
from langchain.callbacks.manager import AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun
from langchain.chat_models.base import BaseChatModel
from langchain.schema import ChatGeneration, ChatResult
from langchain.schema.messages import (AIMessage, BaseMessage, ChatMessage, FunctionMessage,
HumanMessage, SystemMessage)
from langchain.utils import get_from_dict_or_env
from pydantic import Field, root_validator
from tenacity import (before_sleep_log, retry, retry_if_exception_type, stop_after_attempt,
wait_exponential)
if TYPE_CHECKING:
import tiktoken
logger = logging.getLogger(__name__)
def _import_tiktoken() -> Any:
try:
import tiktoken
except ImportError:
raise ValueError('Could not import tiktoken python package. '
'This is needed in order to calculate get_token_ids. '
'Please install it with `pip install tiktoken`.')
return tiktoken
def _create_retry_decorator(llm: ProxyChatLLM) -> Callable[[Any], Any]:
min_seconds = 1
max_seconds = 20
# Wait 2^x * 1 second between each retry starting with
# 4 seconds, then up to 10 seconds, then 10 seconds afterwards
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(Exception)),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
role = _dict['role']
if role == 'user':
return HumanMessage(content=_dict['content'])
elif role == 'assistant':
content = _dict['content'] or '' # OpenAI returns None for tool invocations
if _dict.get('function_call'):
additional_kwargs = {'function_call': dict(_dict['function_call'])}
else:
additional_kwargs = {}
return AIMessage(content=content, additional_kwargs=additional_kwargs)
elif role == 'system':
return SystemMessage(content=_dict['content'])
elif role == 'function':
return FunctionMessage(content=_dict['content'], name=_dict['name'])
else:
return ChatMessage(content=_dict['content'], role=role)
def _convert_message_to_dict(message: BaseMessage) -> dict:
if isinstance(message, ChatMessage):
message_dict = {'role': message.role, 'content': message.content}
elif isinstance(message, HumanMessage):
message_dict = {'role': 'user', 'content': message.content}
elif isinstance(message, AIMessage):
message_dict = {'role': 'assistant', 'content': message.content}
if 'function_call' in message.additional_kwargs:
message_dict['function_call'] = message.additional_kwargs['function_call']
elif isinstance(message, SystemMessage):
message_dict = {'role': 'system', 'content': message.content}
elif isinstance(message, FunctionMessage):
message_dict = {
'role': 'function',
'content': message.content,
'name': message.name,
}
else:
raise ValueError(f'Got unknown type {message}')
if 'name' in message.additional_kwargs:
message_dict['name'] = message.additional_kwargs['name']
return message_dict
class ProxyChatLLM(BaseChatModel):
"""Wrapper around proxy Chat large language models.
To use, the environment variable ``ELEMAI_API_KEY`` set with your API key.
Example:
.. code-block:: python
from bisheng_langchain.chat_models import ProxyChatLLM
proxy_chat_llm = ProxyChatLLM(model_name="chatglm_std")
"""
client: Optional[Any] #: :meta private:
"""Model name to use."""
model_name: str = Field('chatglm_std', alias='model')
temperature: float = 0.7
top_p: float = 0.9
"""What sampling temperature to use."""
model_kwargs: Optional[Dict[str, Any]] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
elemai_api_key: Optional[str] = None
elemai_base_url: Optional[str] = None
headers: Optional[Dict[str, str]] = Field(default_factory=dict)
request_timeout: Optional[Union[float, Tuple[float, float]]] = None
"""Timeout for requests to OpenAI completion API. Default is 600 seconds."""
max_retries: Optional[int] = 0
"""Maximum number of retries to make when generating."""
streaming: Optional[bool] = False
"""Whether to stream the results or not."""
n: Optional[int] = 1
"""Number of chat completions to generate for each prompt."""
max_tokens: Optional[int] = 2048
"""Maximum number of tokens to generate."""
tiktoken_model_name: Optional[str] = None
"""The model name to pass to tiktoken when using this class.
Tiktoken is used to count the number of tokens in documents to constrain
them to be under a certain limit. By default, when set to None, this will
be the same as the embedding model name. However, there are some cases
where you may want to use this Embedding class with a model name not
supported by tiktoken. This can include when using Azure embeddings or
when using one of the many model providers that expose an OpenAI-like
API but with different models. In those cases, in order to avoid erroring
when tiktoken is called, you can specify a model name to use here."""
class Config:
"""Configuration for this pydantic object."""
allow_population_by_field_name = True
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
values['elemai_api_key'] = get_from_dict_or_env(values, 'elemai_api_key', 'ELEMAI_API_KEY')
values['elemai_base_url'] = get_from_dict_or_env(values, 'elemai_base_url',
'ELEMAI_BASE_URL')
elemai_api_key = values['elemai_api_key']
values['headers'] = {
'Authorization': f'Bearer {elemai_api_key}',
'Content-Type': 'application/json'
}
try:
values['client'] = requests.Requests(headers=values['headers'])
except AttributeError:
raise ValueError('Try upgrading it with `pip install --upgrade requests`.')
return values
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling ProxyChatLLM API."""
self.client.request_timeout = self.request_timeout
return {
'model': self.model_name,
'temperature': self.temperature,
'top_p': self.top_p,
'max_tokens': self.max_tokens,
**self.model_kwargs,
}
def completion_with_retry(self, **kwargs: Any) -> Any:
retry_decorator = _create_retry_decorator(self)
@retry_decorator
def _completion_with_retry(**kwargs: Any) -> Any:
messages = kwargs.get('messages')
temperature = kwargs.get('temperature')
top_p = kwargs.get('top_p')
max_tokens = kwargs.get('max_tokens')
params = {
'messages': messages,
'model': self.model_name,
'top_p': top_p,
'temperature': temperature,
'max_tokens': max_tokens,
'stop': kwargs.get('stop', None),
'function_call': kwargs.get('function_call', None),
'functions': kwargs.get('functions', [])
}
response = self.client.post(self.elemai_base_url, data=params)
return response.json()
return _completion_with_retry(**kwargs)
def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
overall_token_usage: dict = {}
for output in llm_outputs:
if output is None:
# Happens in streaming
continue
token_usage = output['token_usage']
for k, v in token_usage.items():
if k in overall_token_usage:
overall_token_usage[k] += v
else:
overall_token_usage[k] = v
return {'token_usage': overall_token_usage, 'model_name': self.model_name}
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
message_dicts, params = self._create_message_dicts(messages, stop)
params = {**params, **kwargs}
response = self.completion_with_retry(messages=message_dicts, **params)
return self._create_chat_result(response)
async def acompletion_with_retry(self, **kwargs: Any) -> Any:
"""Use tenacity to retry the async completion call."""
retry_decorator = _create_retry_decorator(self)
@retry_decorator
async def _acompletion_with_retry(**kwargs: Any) -> Any:
# Use OpenAI's async api https://github.com/openai/openai-python#async-api
async with self.client.apost(url=self.elemai_base_url, data=kwargs) as response:
async for txt in response.content.iter_any():
if b'\n' in txt:
for txt_ in txt.split(b'\n'):
yield txt_.decode('utf-8').strip()
else:
yield txt.decode('utf-8').strip()
async for response in _acompletion_with_retry(**kwargs):
if response:
yield json.loads(response)
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
message_dicts, params = self._create_message_dicts(messages, stop)
params = {**params, **kwargs}
if self.streaming:
inner_completion = ''
role = 'assistant'
params['stream'] = True
function_call: Optional[dict] = None
async for stream_resp in self.acompletion_with_retry(messages=message_dicts, **params):
role = stream_resp['choices'][0]['delta'].get('role', role)
token = stream_resp['choices'][0]['delta'].get('content', '')
inner_completion += token or ''
_function_call = stream_resp['choices'][0]['delta'].get('function_call')
if _function_call:
if function_call is None:
function_call = _function_call
else:
function_call['arguments'] += _function_call['arguments']
if run_manager:
await run_manager.on_llm_new_token(token)
message = _convert_dict_to_message({
'content': inner_completion,
'role': role,
'function_call': function_call,
})
return ChatResult(generations=[ChatGeneration(message=message)])
else:
response = await self.acompletion_with_retry(messages=message_dicts, **params)
return self._create_chat_result(response)
def _create_message_dicts(
self, messages: List[BaseMessage],
stop: Optional[List[str]]) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
params = dict(self._client_params)
if stop is not None:
if 'stop' in params:
raise ValueError('`stop` found in both the input and default params.')
params['stop'] = stop
message_dicts = [_convert_message_to_dict(m) for m in messages]
return message_dicts, params
def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
generations = []
if 'choices' not in response:
raise Exception(f'LLM return error {response}')
for res in response['choices']:
message = _convert_dict_to_message(res['message'])
gen = ChatGeneration(message=message)
generations.append(gen)
llm_output = {'token_usage': response['usage'], 'model_name': self.model_name}
return ChatResult(generations=generations, llm_output=llm_output)
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {**{'model_name': self.model_name}, **self._default_params}
@property
def _client_params(self) -> Mapping[str, Any]:
"""Get the parameters used for the elemai client."""
elemai_creds: Dict[str, Any] = {
'api_key': self.elemai_api_key,
'base_url': self.elemai_base_url,
'model': self.model_name,
}
return {**elemai_creds, **self._default_params}
def _get_invocation_params(self,
stop: Optional[List[str]] = None,
**kwargs: Any) -> Dict[str, Any]:
"""Get the parameters used to invoke the model FOR THE CALLBACKS."""
return {
**super()._get_invocation_params(stop=stop, **kwargs),
**self._default_params,
'model': self.model_name,
'function': kwargs.get('functions'),
}
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
return 'proxy-chat'
def _get_encoding_model(self) -> Tuple[str, tiktoken.Encoding]:
tiktoken_ = _import_tiktoken()
if self.tiktoken_model_name is not None:
model = self.tiktoken_model_name
else:
model = self.model_name
# model chatglm-std, chatglm-lite
# Returns the number of tokens used by a list of messages.
try:
encoding = tiktoken_.encoding_for_model(model)
except KeyError:
logger.warning('Warning: model not found. Using cl100k_base encoding.')
model = 'cl100k_base'
encoding = tiktoken_.get_encoding(model)
return model, encoding
def get_token_ids(self, text: str) -> List[int]:
"""Get the tokens present in the text with tiktoken package."""
# tiktoken NOT supported for Python 3.7 or below
if sys.version_info[1] <= 7:
return super().get_token_ids(text)
_, encoding_model = self._get_encoding_model()
return encoding_model.encode(text)
def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:
"""Calculate num tokens for chatglm with tiktoken package.
todo: read chatglm document
Official documentation: https://github.com/openai/openai-cookbook/blob/
main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
if sys.version_info[1] <= 7:
return super().get_num_tokens_from_messages(messages)
model, encoding = self._get_encoding_model()
if model.startswith('chatglm'):
# every message follows <im_start>{role/name}\n{content}<im_end>\n
tokens_per_message = 4
# if there's a name, the role is omitted
tokens_per_name = -1
else:
raise NotImplementedError(
f'get_num_tokens_from_messages() is not presently implemented '
f'for model {model}.'
'See https://github.com/openai/openai-python/blob/main/chatml.md for '
'information on how messages are converted to tokens.')
num_tokens = 0
messages_dict = [_convert_message_to_dict(m) for m in messages]
for message in messages_dict:
num_tokens += tokens_per_message
for key, value in message.items():
num_tokens += len(encoding.encode(value))
if key == 'name':
num_tokens += tokens_per_name
# every reply is primed with <im_start>assistant
num_tokens += 3
return num_tokens