Source code for langchain_community.chat_models.premai

"""Wrapper around Prem's Chat API."""

from __future__ import annotations

import logging
from typing import (

from langchain_core.callbacks import (
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.language_models.llms import create_base_retry_decorator
from langchain_core.messages import (
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.pydantic_v1 import (
from langchain_core.utils import get_from_dict_or_env

    from premai.api.chat_completions.v1_chat_completions_create import (
    from premai.models.chat_completion_response import ChatCompletionResponse

logger = logging.getLogger(__name__)

[docs]class ChatPremAPIError(Exception): """Error with the `PremAI` API."""
def _truncate_at_stop_tokens( text: str, stop: Optional[List[str]], ) -> str: """Truncates text at the earliest stop token found.""" if stop is None: return text for stop_token in stop: stop_token_idx = text.find(stop_token) if stop_token_idx != -1: text = text[:stop_token_idx] return text def _response_to_result( response: ChatCompletionResponse, stop: Optional[List[str]], ) -> ChatResult: """Converts a Prem API response into a LangChain result""" if not response.choices: raise ChatPremAPIError("ChatResponse must have at least one candidate") generations: List[ChatGeneration] = [] for choice in response.choices: role = choice.message.role if role is None: raise ChatPremAPIError(f"ChatResponse {choice} must have a role.") # If content is None then it will be replaced by "" content = _truncate_at_stop_tokens(text=choice.message.content or "", stop=stop) if content is None: raise ChatPremAPIError(f"ChatResponse must have a content: {content}") if role == "assistant": generations.append( ChatGeneration(text=content, message=AIMessage(content=content)) ) elif role == "user": generations.append( ChatGeneration(text=content, message=HumanMessage(content=content)) ) else: generations.append( ChatGeneration( text=content, message=ChatMessage(role=role, content=content) ) ) return ChatResult(generations=generations) def _convert_delta_response_to_message_chunk( response: ChatCompletionResponseStream, default_class: Type[BaseMessageChunk] ) -> Tuple[ Union[BaseMessageChunk, HumanMessageChunk, AIMessageChunk, SystemMessageChunk], Optional[str], ]: """Converts delta response to message chunk""" _delta = response.choices[0].delta # type: ignore role = _delta.get("role", "") # type: ignore content = _delta.get("content", "") # type: ignore additional_kwargs: Dict = {} if role is None or role == "": raise ChatPremAPIError("Role can not be None. Please check the response") finish_reasons: Optional[str] = response.choices[0].finish_reason if role == "user" or default_class == HumanMessageChunk: return HumanMessageChunk(content=content), finish_reasons elif role == "assistant" or default_class == AIMessageChunk: return ( AIMessageChunk(content=content, additional_kwargs=additional_kwargs), finish_reasons, ) elif role == "system" or default_class == SystemMessageChunk: return SystemMessageChunk(content=content), finish_reasons elif role or default_class == ChatMessageChunk: return ChatMessageChunk(content=content, role=role), finish_reasons else: return default_class(content=content), finish_reasons # type: ignore[call-arg] def _messages_to_prompt_dict( input_messages: List[BaseMessage], ) -> Tuple[Optional[str], List[Dict[str, str]]]: """Converts a list of LangChain Messages into a simple dict which is the message structure in Prem""" system_prompt: Optional[str] = None examples_and_messages: List[Dict[str, str]] = [] for input_msg in input_messages: if isinstance(input_msg, SystemMessage): system_prompt = str(input_msg.content) elif isinstance(input_msg, HumanMessage): examples_and_messages.append( {"role": "user", "content": str(input_msg.content)} ) elif isinstance(input_msg, AIMessage): examples_and_messages.append( {"role": "assistant", "content": str(input_msg.content)} ) else: raise ChatPremAPIError("No such role explicitly exists") return system_prompt, examples_and_messages
[docs]class ChatPremAI(BaseChatModel, BaseModel): """PremAI Chat models. To use, you will need to have an API key. You can find your existing API Key or generate a new one here: """ # TODO: Need to add the default parameters through prem-sdk here project_id: int """The project ID in which the experiments or deployments are carried out. You can find all your projects here:""" premai_api_key: Optional[SecretStr] = Field(default=None, alias="api_key") """Prem AI API Key. Get it here:""" model: Optional[str] = Field(default=None, alias="model_name") """Name of the model. This is an optional parameter. The default model is the one deployed from Prem's LaunchPad: If model name is other than default model then it will override the calls from the model deployed from launchpad.""" session_id: Optional[str] = None """The ID of the session to use. It helps to track the chat history.""" temperature: Optional[float] = None """Model temperature. Value should be >= 0 and <= 1.0""" top_p: Optional[float] = None """top_p adjusts the number of choices for each predicted tokens based on cumulative probabilities. Value should be ranging between 0.0 and 1.0. """ max_tokens: Optional[int] = None """The maximum number of tokens to generate""" max_retries: int = 1 """Max number of retries to call the API""" system_prompt: Optional[str] = "" """Acts like a default instruction that helps the LLM act or generate in a specific way.This is an Optional Parameter. By default the system prompt would be using Prem's Launchpad models system prompt. Changing the system prompt would override the default system prompt. """ streaming: Optional[bool] = False """Whether to stream the responses or not.""" tools: Optional[Dict[str, Any]] = None """A list of tools the model may call. Currently, only functions are supported as a tool""" frequency_penalty: Optional[float] = None """Number between -2.0 and 2.0. Positive values penalize new tokens based""" presence_penalty: Optional[float] = None """Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far.""" logit_bias: Optional[dict] = None """JSON object that maps tokens to an associated bias value from -100 to 100.""" stop: Optional[Union[str, List[str]]] = None """Up to 4 sequences where the API will stop generating further tokens.""" seed: Optional[int] = None """This feature is in Beta. If specified, our system will make a best effort to sample deterministically.""" client: Any class Config: """Configuration for this pydantic object.""" extra = Extra.forbid allow_population_by_field_name = True arbitrary_types_allowed = True @root_validator() def validate_environments(cls, values: Dict) -> Dict: """Validate that the package is installed and that the API token is valid""" try: from premai import Prem except ImportError as error: raise ImportError( "Could not import Prem Python package." "Please install it with: `pip install premai`" ) from error try: premai_api_key = get_from_dict_or_env( values, "premai_api_key", "PREMAI_API_KEY" ) values["client"] = Prem(api_key=premai_api_key) except Exception as error: raise ValueError("Your API Key is incorrect. Please try again.") from error return values @property def _llm_type(self) -> str: return "premai" @property def _default_params(self) -> Dict[str, Any]: # FIXME: n and stop is not supported, so hardcoding to current default value return { "model": self.model, "system_prompt": self.system_prompt, "top_p": self.top_p, "temperature": self.temperature, "logit_bias": self.logit_bias, "max_tokens": self.max_tokens, "presence_penalty": self.presence_penalty, "frequency_penalty": self.frequency_penalty, "seed": self.seed, "stop": None, } def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]: all_kwargs = {**self._default_params, **kwargs} for key in list(self._default_params.keys()): if all_kwargs.get(key) is None or all_kwargs.get(key) == "": all_kwargs.pop(key, None) return all_kwargs def _generate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> ChatResult: system_prompt, messages_to_pass = _messages_to_prompt_dict(messages) # type: ignore kwargs["stop"] = stop if system_prompt is not None and system_prompt != "": kwargs["system_prompt"] = system_prompt all_kwargs = self._get_all_kwargs(**kwargs) response = chat_with_retry( self, project_id=self.project_id, messages=messages_to_pass, stream=False, run_manager=run_manager, **all_kwargs, ) return _response_to_result(response=response, stop=stop) def _stream( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> Iterator[ChatGenerationChunk]: system_prompt, messages_to_pass = _messages_to_prompt_dict(messages) kwargs["stop"] = stop if "system_prompt" not in kwargs: if system_prompt is not None and system_prompt != "": kwargs["system_prompt"] = system_prompt all_kwargs = self._get_all_kwargs(**kwargs) default_chunk_class = AIMessageChunk for streamed_response in chat_with_retry( self, project_id=self.project_id, messages=messages_to_pass, stream=True, run_manager=run_manager, **all_kwargs, ): try: chunk, finish_reason = _convert_delta_response_to_message_chunk( response=streamed_response, default_class=default_chunk_class ) generation_info = ( dict(finish_reason=finish_reason) if finish_reason is not None else None ) cg_chunk = ChatGenerationChunk( message=chunk, generation_info=generation_info ) if run_manager: run_manager.on_llm_new_token(cg_chunk.text, chunk=cg_chunk) yield cg_chunk except Exception as _: continue
[docs]def create_prem_retry_decorator( llm: ChatPremAI, *, max_retries: int = 1, run_manager: Optional[Union[CallbackManagerForLLMRun]] = None, ) -> Callable[[Any], Any]: """Create a retry decorator for PremAI API errors.""" import premai.models errors = [ premai.models.api_response_validation_error.APIResponseValidationError, premai.models.conflict_error.ConflictError, premai.models.model_not_found_error.ModelNotFoundError, premai.models.permission_denied_error.PermissionDeniedError, premai.models.provider_api_connection_error.ProviderAPIConnectionError, premai.models.provider_api_status_error.ProviderAPIStatusError, premai.models.provider_api_timeout_error.ProviderAPITimeoutError, premai.models.provider_internal_server_error.ProviderInternalServerError, premai.models.provider_not_found_error.ProviderNotFoundError, premai.models.rate_limit_error.RateLimitError, premai.models.unprocessable_entity_error.UnprocessableEntityError, premai.models.validation_error.ValidationError, ] decorator = create_base_retry_decorator( error_types=errors, max_retries=max_retries, run_manager=run_manager ) return decorator
[docs]def chat_with_retry( llm: ChatPremAI, project_id: int, messages: List[dict], stream: bool = False, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> Any: """Using tenacity for retry in completion call""" retry_decorator = create_prem_retry_decorator( llm, max_retries=llm.max_retries, run_manager=run_manager ) @retry_decorator def _completion_with_retry( project_id: int, messages: List[dict], stream: Optional[bool] = False, **kwargs: Any, ) -> Any: response = project_id=project_id, messages=messages, stream=stream, **kwargs, ) return response return _completion_with_retry( project_id=project_id, messages=messages, stream=stream, **kwargs, )