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272 lines
10 KiB
Python
272 lines
10 KiB
Python
"""Wrapper around Perplexity APIs."""
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from __future__ import annotations
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import logging
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from typing import (
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Any,
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Dict,
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Iterator,
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List,
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Mapping,
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Optional,
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Tuple,
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Type,
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Union,
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)
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from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.language_models.chat_models import (
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BaseChatModel,
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generate_from_stream,
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)
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from langchain_core.messages import (
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AIMessage,
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AIMessageChunk,
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BaseMessage,
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BaseMessageChunk,
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ChatMessage,
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ChatMessageChunk,
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FunctionMessageChunk,
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HumanMessage,
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HumanMessageChunk,
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SystemMessage,
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SystemMessageChunk,
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ToolMessageChunk,
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)
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from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
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from langchain_core.pydantic_v1 import Field, root_validator
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from langchain_core.utils import get_from_dict_or_env, get_pydantic_field_names
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logger = logging.getLogger(__name__)
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class ChatPerplexity(BaseChatModel):
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"""`Perplexity AI` Chat models API.
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To use, you should have the ``openai`` python package installed, and the
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environment variable ``PPLX_API_KEY`` set to your API key.
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Any parameters that are valid to be passed to the openai.create call can be passed
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in, even if not explicitly saved on this class.
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Example:
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.. code-block:: python
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from langchain_community.chat_models import ChatPerplexity
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chat = ChatPerplexity(model="pplx-70b-online", temperature=0.7)
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"""
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client: Any #: :meta private:
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model: str = "pplx-70b-online"
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"""Model name."""
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temperature: float = 0.7
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"""What sampling temperature to use."""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Holds any model parameters valid for `create` call not explicitly specified."""
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pplx_api_key: Optional[str] = None
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"""Base URL path for API requests,
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leave blank if not using a proxy or service emulator."""
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request_timeout: Optional[Union[float, Tuple[float, float]]] = None
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"""Timeout for requests to PerplexityChat completion API. Default is 600 seconds."""
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max_retries: int = 6
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"""Maximum number of retries to make when generating."""
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streaming: bool = False
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"""Whether to stream the results or not."""
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max_tokens: Optional[int] = None
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"""Maximum number of tokens to generate."""
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class Config:
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"""Configuration for this pydantic object."""
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allow_population_by_field_name = True
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@property
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def lc_secrets(self) -> Dict[str, str]:
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return {"pplx_api_key": "PPLX_API_KEY"}
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@root_validator(pre=True, allow_reuse=True)
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def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
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"""Build extra kwargs from additional params that were passed in."""
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all_required_field_names = get_pydantic_field_names(cls)
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extra = values.get("model_kwargs", {})
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for field_name in list(values):
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if field_name in extra:
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raise ValueError(f"Found {field_name} supplied twice.")
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if field_name not in all_required_field_names:
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logger.warning(
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f"""WARNING! {field_name} is not a default parameter.
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{field_name} was transferred to model_kwargs.
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Please confirm that {field_name} is what you intended."""
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)
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extra[field_name] = values.pop(field_name)
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invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
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if invalid_model_kwargs:
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raise ValueError(
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f"Parameters {invalid_model_kwargs} should be specified explicitly. "
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f"Instead they were passed in as part of `model_kwargs` parameter."
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)
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values["model_kwargs"] = extra
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return values
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@root_validator(allow_reuse=True)
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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values["pplx_api_key"] = get_from_dict_or_env(
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values, "pplx_api_key", "PPLX_API_KEY"
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)
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try:
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import openai # noqa: F401
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except ImportError:
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raise ImportError(
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"Could not import openai python package. "
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"Please install it with `pip install openai`."
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)
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try:
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values["client"] = openai.OpenAI(
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api_key=values["pplx_api_key"], base_url="https://api.perplexity.ai"
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)
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except AttributeError:
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raise ValueError(
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"`openai` has no `ChatCompletion` attribute, this is likely "
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"due to an old version of the openai package. Try upgrading it "
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"with `pip install --upgrade openai`."
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)
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return values
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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Get the default parameters for calling PerplexityChat API."""
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return {
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"request_timeout": self.request_timeout,
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"max_tokens": self.max_tokens,
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"stream": self.streaming,
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"temperature": self.temperature,
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**self.model_kwargs,
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}
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def _convert_message_to_dict(self, message: BaseMessage) -> Dict[str, Any]:
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if isinstance(message, ChatMessage):
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message_dict = {"role": message.role, "content": message.content}
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elif isinstance(message, SystemMessage):
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message_dict = {"role": "system", "content": message.content}
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elif isinstance(message, HumanMessage):
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message_dict = {"role": "user", "content": message.content}
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elif isinstance(message, AIMessage):
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message_dict = {"role": "assistant", "content": message.content}
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else:
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raise TypeError(f"Got unknown type {message}")
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return message_dict
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def _create_message_dicts(
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self, messages: List[BaseMessage], stop: Optional[List[str]]
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) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
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params = dict(self._invocation_params)
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if stop is not None:
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if "stop" in params:
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raise ValueError("`stop` found in both the input and default params.")
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params["stop"] = stop
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message_dicts = [self._convert_message_to_dict(m) for m in messages]
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return message_dicts, params
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def _convert_delta_to_message_chunk(
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self, _dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
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) -> BaseMessageChunk:
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role = _dict.get("role")
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content = _dict.get("content") or ""
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additional_kwargs: Dict = {}
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if _dict.get("function_call"):
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function_call = dict(_dict["function_call"])
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if "name" in function_call and function_call["name"] is None:
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function_call["name"] = ""
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additional_kwargs["function_call"] = function_call
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if _dict.get("tool_calls"):
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additional_kwargs["tool_calls"] = _dict["tool_calls"]
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if role == "user" or default_class == HumanMessageChunk:
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return HumanMessageChunk(content=content)
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elif role == "assistant" or default_class == AIMessageChunk:
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return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
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elif role == "system" or default_class == SystemMessageChunk:
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return SystemMessageChunk(content=content)
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elif role == "function" or default_class == FunctionMessageChunk:
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return FunctionMessageChunk(content=content, name=_dict["name"])
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elif role == "tool" or default_class == ToolMessageChunk:
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return ToolMessageChunk(content=content, tool_call_id=_dict["tool_call_id"])
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elif role or default_class == ChatMessageChunk:
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return ChatMessageChunk(content=content, role=role)
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else:
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return default_class(content=content)
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def _stream(
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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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) -> Iterator[ChatGenerationChunk]:
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message_dicts, params = self._create_message_dicts(messages, stop)
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params = {**params, **kwargs}
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default_chunk_class = AIMessageChunk
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if stop:
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params["stop_sequences"] = stop
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stream_resp = self.client.chat.completions.create(
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model=params["model"], messages=message_dicts, stream=True
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)
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for chunk in stream_resp:
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if not isinstance(chunk, dict):
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chunk = chunk.dict()
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if len(chunk["choices"]) == 0:
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continue
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choice = chunk["choices"][0]
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chunk = self._convert_delta_to_message_chunk(
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choice["delta"], default_chunk_class
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)
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finish_reason = choice.get("finish_reason")
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generation_info = (
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dict(finish_reason=finish_reason) if finish_reason is not None else None
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)
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default_chunk_class = chunk.__class__
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chunk = ChatGenerationChunk(message=chunk, generation_info=generation_info)
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if run_manager:
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run_manager.on_llm_new_token(chunk.text, chunk=chunk)
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yield chunk
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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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if self.streaming:
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stream_iter = self._stream(
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messages, stop=stop, run_manager=run_manager, **kwargs
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)
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if stream_iter:
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return generate_from_stream(stream_iter)
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message_dicts, params = self._create_message_dicts(messages, stop)
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params = {**params, **kwargs}
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response = self.client.chat.completions.create(
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model=params["model"], messages=message_dicts
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)
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message = AIMessage(content=response.choices[0].message.content)
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return ChatResult(generations=[ChatGeneration(message=message)])
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@property
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def _invocation_params(self) -> Mapping[str, Any]:
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"""Get the parameters used to invoke the model."""
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pplx_creds: Dict[str, Any] = {
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"api_key": self.pplx_api_key,
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"api_base": "https://api.perplexity.ai",
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"model": self.model,
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}
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return {**pplx_creds, **self._default_params}
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@property
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def _llm_type(self) -> str:
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"""Return type of chat model."""
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return "perplexitychat"
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