"""Wrapper around Perplexity APIs."""
from __future__ import annotations
import logging
from typing import (
Any,
Dict,
Iterator,
List,
Mapping,
Optional,
Tuple,
Type,
Union,
)
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.chat_models import (
BaseChatModel,
generate_from_stream,
)
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
BaseMessageChunk,
ChatMessage,
ChatMessageChunk,
FunctionMessageChunk,
HumanMessage,
HumanMessageChunk,
SystemMessage,
SystemMessageChunk,
ToolMessageChunk,
)
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.utils import (
from_env,
get_pydantic_field_names,
)
from pydantic import ConfigDict, Field, model_validator
from typing_extensions import Self
logger = logging.getLogger(__name__)
[docs]
class ChatPerplexity(BaseChatModel):
"""`Perplexity AI` Chat models API.
To use, you should have the ``openai`` python package installed, and the
environment variable ``PPLX_API_KEY`` set to your API key.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example:
.. code-block:: python
from langchain_community.chat_models import ChatPerplexity
chat = ChatPerplexity(
model="llama-3.1-sonar-small-128k-online",
temperature=0.7,
)
"""
client: Any = None #: :meta private:
model: str = "llama-3.1-sonar-small-128k-online"
"""Model name."""
temperature: float = 0.7
"""What sampling temperature to use."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
pplx_api_key: Optional[str] = Field(
default_factory=from_env("PPLX_API_KEY", default=None), alias="api_key"
)
"""Base URL path for API requests,
leave blank if not using a proxy or service emulator."""
request_timeout: Optional[Union[float, Tuple[float, float]]] = Field(
None, alias="timeout"
)
"""Timeout for requests to PerplexityChat completion API. Default is None."""
max_retries: int = 6
"""Maximum number of retries to make when generating."""
streaming: bool = False
"""Whether to stream the results or not."""
max_tokens: Optional[int] = None
"""Maximum number of tokens to generate."""
model_config = ConfigDict(
populate_by_name=True,
)
@property
def lc_secrets(self) -> Dict[str, str]:
return {"pplx_api_key": "PPLX_API_KEY"}
@model_validator(mode="before")
@classmethod
def build_extra(cls, values: Dict[str, Any]) -> Any:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = get_pydantic_field_names(cls)
extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name in extra:
raise ValueError(f"Found {field_name} supplied twice.")
if field_name not in all_required_field_names:
logger.warning(
f"""WARNING! {field_name} is not a default parameter.
{field_name} was transferred to model_kwargs.
Please confirm that {field_name} is what you intended."""
)
extra[field_name] = values.pop(field_name)
invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
if invalid_model_kwargs:
raise ValueError(
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
f"Instead they were passed in as part of `model_kwargs` parameter."
)
values["model_kwargs"] = extra
return values
@model_validator(mode="after")
def validate_environment(self) -> Self:
"""Validate that api key and python package exists in environment."""
try:
import openai
except ImportError:
raise ImportError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
try:
self.client = openai.OpenAI(
api_key=self.pplx_api_key, base_url="https://api.perplexity.ai"
)
except AttributeError:
raise ValueError(
"`openai` has no `ChatCompletion` attribute, this is likely "
"due to an old version of the openai package. Try upgrading it "
"with `pip install --upgrade openai`."
)
return self
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling PerplexityChat API."""
return {
"max_tokens": self.max_tokens,
"stream": self.streaming,
"temperature": self.temperature,
**self.model_kwargs,
}
def _convert_message_to_dict(self, message: BaseMessage) -> Dict[str, Any]:
if isinstance(message, ChatMessage):
message_dict = {"role": message.role, "content": message.content}
elif isinstance(message, SystemMessage):
message_dict = {"role": "system", "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}
else:
raise TypeError(f"Got unknown type {message}")
return message_dict
def _create_message_dicts(
self, messages: List[BaseMessage], stop: Optional[List[str]]
) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
params = dict(self._invocation_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 = [self._convert_message_to_dict(m) for m in messages]
return message_dicts, params
def _convert_delta_to_message_chunk(
self, _dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
) -> BaseMessageChunk:
role = _dict.get("role")
content = _dict.get("content") or ""
additional_kwargs: Dict = {}
if _dict.get("function_call"):
function_call = dict(_dict["function_call"])
if "name" in function_call and function_call["name"] is None:
function_call["name"] = ""
additional_kwargs["function_call"] = function_call
if _dict.get("tool_calls"):
additional_kwargs["tool_calls"] = _dict["tool_calls"]
if role == "user" or default_class == HumanMessageChunk:
return HumanMessageChunk(content=content)
elif role == "assistant" or default_class == AIMessageChunk:
return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
elif role == "system" or default_class == SystemMessageChunk:
return SystemMessageChunk(content=content)
elif role == "function" or default_class == FunctionMessageChunk:
return FunctionMessageChunk(content=content, name=_dict["name"])
elif role == "tool" or default_class == ToolMessageChunk:
return ToolMessageChunk(content=content, tool_call_id=_dict["tool_call_id"])
elif role or default_class == ChatMessageChunk:
return ChatMessageChunk(content=content, role=role) # type: ignore[arg-type]
else:
return default_class(content=content) # type: ignore[call-arg]
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
message_dicts, params = self._create_message_dicts(messages, stop)
params = {**params, **kwargs}
default_chunk_class = AIMessageChunk
if stop:
params["stop_sequences"] = stop
stream_resp = self.client.chat.completions.create(
messages=message_dicts, stream=True, **params
)
for chunk in stream_resp:
if not isinstance(chunk, dict):
chunk = chunk.dict()
if len(chunk["choices"]) == 0:
continue
choice = chunk["choices"][0]
chunk = self._convert_delta_to_message_chunk(
choice["delta"], default_chunk_class
)
finish_reason = choice.get("finish_reason")
generation_info = (
dict(finish_reason=finish_reason) if finish_reason is not None else None
)
default_chunk_class = chunk.__class__
chunk = ChatGenerationChunk(message=chunk, generation_info=generation_info)
if run_manager:
run_manager.on_llm_new_token(chunk.text, chunk=chunk)
yield chunk
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
if self.streaming:
stream_iter = self._stream(
messages, stop=stop, run_manager=run_manager, **kwargs
)
if stream_iter:
return generate_from_stream(stream_iter)
message_dicts, params = self._create_message_dicts(messages, stop)
params = {**params, **kwargs}
response = self.client.chat.completions.create(messages=message_dicts, **params)
message = AIMessage(
content=response.choices[0].message.content,
additional_kwargs={"citations": response.citations},
)
return ChatResult(generations=[ChatGeneration(message=message)])
@property
def _invocation_params(self) -> Mapping[str, Any]:
"""Get the parameters used to invoke the model."""
pplx_creds: Dict[str, Any] = {
"model": self.model,
}
return {**pplx_creds, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
return "perplexitychat"
