mirror of
https://github.com/hwchase17/langchain.git
synced 2025-09-25 21:37:20 +00:00
community[minor]: Add support for Perplexity chat model(#17024)
- **Description:** This PR adds support for [Perplexity AI APIs](https://blog.perplexity.ai/blog/introducing-pplx-api). - **Issues:** None - **Dependencies:** None - **Twitter handle:** [@atherfawaz](https://twitter.com/AtherFawaz) --------- Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: Erick Friis <erick@langchain.dev>
This commit is contained in:
Ather Fawaz
GitHub
@@ -50,6 +50,7 @@ from langchain_community.chat_models.mlflow_ai_gateway import ChatMLflowAIGatewa
|
||||
from langchain_community.chat_models.ollama import ChatOllama
|
||||
from langchain_community.chat_models.openai import ChatOpenAI
|
||||
from langchain_community.chat_models.pai_eas_endpoint import PaiEasChatEndpoint
|
||||
from langchain_community.chat_models.perplexity import ChatPerplexity
|
||||
from langchain_community.chat_models.promptlayer_openai import PromptLayerChatOpenAI
|
||||
from langchain_community.chat_models.sparkllm import ChatSparkLLM
|
||||
from langchain_community.chat_models.tongyi import ChatTongyi
|
||||
@@ -99,6 +100,7 @@ __all__ = [
|
||||
"GPTRouter",
|
||||
"ChatYuan2",
|
||||
"ChatZhipuAI",
|
||||
"ChatPerplexity",
|
||||
"ChatKinetica",
|
||||
"ChatMaritalk",
|
||||
]
|
||||
|
||||
271
libs/community/langchain_community/chat_models/perplexity.py
Normal file
271
libs/community/langchain_community/chat_models/perplexity.py
Normal file
@@ -0,0 +1,271 @@
|
||||
"""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.pydantic_v1 import Field, root_validator
|
||||
from langchain_core.utils import get_from_dict_or_env, get_pydantic_field_names
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
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="pplx-70b-online", temperature=0.7)
|
||||
"""
|
||||
|
||||
client: Any #: :meta private:
|
||||
model: str = "pplx-70b-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] = None
|
||||
"""Base URL path for API requests,
|
||||
leave blank if not using a proxy or service emulator."""
|
||||
request_timeout: Optional[Union[float, Tuple[float, float]]] = None
|
||||
"""Timeout for requests to PerplexityChat completion API. Default is 600 seconds."""
|
||||
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."""
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
allow_population_by_field_name = True
|
||||
|
||||
@property
|
||||
def lc_secrets(self) -> Dict[str, str]:
|
||||
return {"pplx_api_key": "PPLX_API_KEY"}
|
||||
|
||||
@root_validator(pre=True, allow_reuse=True)
|
||||
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, 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
|
||||
|
||||
@root_validator(allow_reuse=True)
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validate that api key and python package exists in environment."""
|
||||
values["pplx_api_key"] = get_from_dict_or_env(
|
||||
values, "pplx_api_key", "PPLX_API_KEY"
|
||||
)
|
||||
try:
|
||||
import openai # noqa: F401
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"Could not import openai python package. "
|
||||
"Please install it with `pip install openai`."
|
||||
)
|
||||
try:
|
||||
values["client"] = openai.OpenAI(
|
||||
api_key=values["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 values
|
||||
|
||||
@property
|
||||
def _default_params(self) -> Dict[str, Any]:
|
||||
"""Get the default parameters for calling PerplexityChat API."""
|
||||
return {
|
||||
"request_timeout": self.request_timeout,
|
||||
"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)
|
||||
else:
|
||||
return default_class(content=content)
|
||||
|
||||
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(
|
||||
model=params["model"], messages=message_dicts, stream=True
|
||||
)
|
||||
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)
|
||||
yield chunk
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(chunk.text, chunk=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(
|
||||
model=params["model"], messages=message_dicts
|
||||
)
|
||||
message = AIMessage(content=response.choices[0].message.content)
|
||||
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] = {
|
||||
"api_key": self.pplx_api_key,
|
||||
"api_base": "https://api.perplexity.ai",
|
||||
"model": self.model,
|
||||
}
|
||||
return {**pplx_creds, **self._default_params}
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""Return type of chat model."""
|
||||
return "perplexitychat"
|
||||
@@ -41,6 +41,7 @@ EXPECTED_ALL = [
|
||||
"GPTRouter",
|
||||
"ChatYuan2",
|
||||
"ChatZhipuAI",
|
||||
"ChatPerplexity",
|
||||
"ChatKinetica",
|
||||
]
|
||||
|
||||
|
||||
@@ -0,0 +1,30 @@
|
||||
"""Test Perplexity Chat API wrapper."""
|
||||
import os
|
||||
|
||||
import pytest
|
||||
|
||||
from langchain_community.chat_models import ChatPerplexity
|
||||
|
||||
os.environ["PPLX_API_KEY"] = "foo"
|
||||
|
||||
|
||||
@pytest.mark.requires("openai")
|
||||
def test_perplexity_model_name_param() -> None:
|
||||
llm = ChatPerplexity(model="foo")
|
||||
assert llm.model == "foo"
|
||||
|
||||
|
||||
@pytest.mark.requires("openai")
|
||||
def test_perplexity_model_kwargs() -> None:
|
||||
llm = ChatPerplexity(model="test", model_kwargs={"foo": "bar"})
|
||||
assert llm.model_kwargs == {"foo": "bar"}
|
||||
|
||||
|
||||
@pytest.mark.requires("openai")
|
||||
def test_perplexity_initialization() -> None:
|
||||
"""Test perplexity initialization."""
|
||||
# Verify that chat perplexity can be initialized using a secret key provided
|
||||
# as a parameter rather than an environment variable.
|
||||
ChatPerplexity(
|
||||
model="test", perplexity_api_key="test", temperature=0.7, verbose=True
|
||||
)
|
||||
Reference in New Issue
Block a user