mirror of
https://github.com/hwchase17/langchain.git
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498 lines
19 KiB
Python
498 lines
19 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 collections.abc import Iterator, Mapping
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from operator import itemgetter
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from typing import Any, Literal, TypeAlias
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import openai
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from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.language_models import LanguageModelInput
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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.messages.ai import UsageMetadata, subtract_usage
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from langchain_core.output_parsers import JsonOutputParser, PydanticOutputParser
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from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
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from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
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from langchain_core.utils import get_pydantic_field_names, secret_from_env
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from langchain_core.utils.function_calling import convert_to_json_schema
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from langchain_core.utils.pydantic import is_basemodel_subclass
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from pydantic import BaseModel, ConfigDict, Field, SecretStr, model_validator
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from typing_extensions import Self
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_DictOrPydanticClass: TypeAlias = dict[str, Any] | type[BaseModel]
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_DictOrPydantic: TypeAlias = dict | BaseModel
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logger = logging.getLogger(__name__)
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def _is_pydantic_class(obj: Any) -> bool:
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return isinstance(obj, type) and is_basemodel_subclass(obj)
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def _create_usage_metadata(token_usage: dict) -> UsageMetadata:
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input_tokens = token_usage.get("prompt_tokens", 0)
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output_tokens = token_usage.get("completion_tokens", 0)
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total_tokens = token_usage.get("total_tokens", input_tokens + output_tokens)
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return UsageMetadata(
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input_tokens=input_tokens,
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output_tokens=output_tokens,
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total_tokens=total_tokens,
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)
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class ChatPerplexity(BaseChatModel):
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"""`Perplexity AI` Chat models API.
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Setup:
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To use, you should have the 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
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can be passed in, even if not explicitly saved on this class.
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```bash
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export PPLX_API_KEY=your_api_key
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```
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Key init args - completion params:
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model:
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Name of the model to use. e.g. "sonar"
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temperature:
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Sampling temperature to use.
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max_tokens:
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Maximum number of tokens to generate.
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streaming:
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Whether to stream the results or not.
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Key init args - client params:
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pplx_api_key:
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API key for PerplexityChat API.
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request_timeout:
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Timeout for requests to PerplexityChat completion API.
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max_retries:
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Maximum number of retries to make when generating.
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See full list of supported init args and their descriptions in the params section.
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Instantiate:
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```python
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from langchain_perplexity import ChatPerplexity
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model = ChatPerplexity(model="sonar", temperature=0.7)
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```
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Invoke:
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```python
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messages = [("system", "You are a chatbot."), ("user", "Hello!")]
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model.invoke(messages)
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```
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Invoke with structured output:
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```python
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from pydantic import BaseModel
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class StructuredOutput(BaseModel):
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role: str
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content: str
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model.with_structured_output(StructuredOutput)
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model.invoke(messages)
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```
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Invoke with perplexity-specific params:
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```python
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model.invoke(messages, extra_body={"search_recency_filter": "week"})
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```
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Stream:
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```python
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for chunk in model.stream(messages):
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print(chunk.content)
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```
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Token usage:
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```python
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response = model.invoke(messages)
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response.usage_metadata
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```
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Response metadata:
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```python
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response = model.invoke(messages)
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response.response_metadata
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```
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""" # noqa: E501
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client: Any = None #: :meta private:
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model: str = "sonar"
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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: SecretStr | None = Field(
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default_factory=secret_from_env("PPLX_API_KEY", default=None), alias="api_key"
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)
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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: float | tuple[float, float] | None = Field(None, alias="timeout")
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"""Timeout for requests to PerplexityChat completion API."""
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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: int | None = None
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"""Maximum number of tokens to generate."""
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model_config = ConfigDict(populate_by_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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@model_validator(mode="before")
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@classmethod
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def build_extra(cls, values: dict[str, Any]) -> 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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@model_validator(mode="after")
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def validate_environment(self) -> Self:
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"""Validate that api key and python package exists in environment."""
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try:
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self.client = openai.OpenAI(
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api_key=self.pplx_api_key.get_secret_value()
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if self.pplx_api_key
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else None,
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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 self
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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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"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: list[str] | None
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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) # type: ignore[arg-type]
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else:
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return default_class(content=content) # type: ignore[call-arg]
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def _stream(
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self,
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messages: list[BaseMessage],
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stop: list[str] | None = None,
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run_manager: CallbackManagerForLLMRun | None = 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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params.pop("stream", None)
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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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messages=message_dicts, stream=True, **params
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)
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first_chunk = True
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prev_total_usage: UsageMetadata | None = None
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added_model_name: bool = False
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for chunk in stream_resp:
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if not isinstance(chunk, dict):
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chunk = chunk.model_dump()
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# Collect standard usage metadata (transform from aggregate to delta)
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if total_usage := chunk.get("usage"):
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lc_total_usage = _create_usage_metadata(total_usage)
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if prev_total_usage:
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usage_metadata: UsageMetadata | None = subtract_usage(
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lc_total_usage, prev_total_usage
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)
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else:
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usage_metadata = lc_total_usage
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prev_total_usage = lc_total_usage
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else:
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usage_metadata = None
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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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additional_kwargs = {}
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if first_chunk:
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additional_kwargs["citations"] = chunk.get("citations", [])
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for attr in ["images", "related_questions", "search_results"]:
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if attr in chunk:
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additional_kwargs[attr] = chunk[attr]
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generation_info = {}
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if (model_name := chunk.get("model")) and not added_model_name:
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generation_info["model_name"] = model_name
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added_model_name = True
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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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if isinstance(chunk, AIMessageChunk) and usage_metadata:
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chunk.usage_metadata = usage_metadata
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if first_chunk:
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chunk.additional_kwargs |= additional_kwargs
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first_chunk = False
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if finish_reason := choice.get("finish_reason"):
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generation_info["finish_reason"] = finish_reason
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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: list[str] | None = None,
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run_manager: CallbackManagerForLLMRun | None = 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(messages=message_dicts, **params)
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if usage := getattr(response, "usage", None):
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usage_metadata = _create_usage_metadata(usage.model_dump())
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else:
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usage_metadata = None
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additional_kwargs = {}
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for attr in ["citations", "images", "related_questions", "search_results"]:
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if hasattr(response, attr):
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additional_kwargs[attr] = getattr(response, attr)
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message = AIMessage(
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content=response.choices[0].message.content,
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additional_kwargs=additional_kwargs,
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usage_metadata=usage_metadata,
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response_metadata={"model_name": getattr(response, "model", self.model)},
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)
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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] = {"model": self.model}
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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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def with_structured_output(
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self,
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schema: _DictOrPydanticClass | None = None,
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*,
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method: Literal["json_schema"] = "json_schema",
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include_raw: bool = False,
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strict: bool | None = None,
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**kwargs: Any,
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) -> Runnable[LanguageModelInput, _DictOrPydantic]:
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"""Model wrapper that returns outputs formatted to match the given schema for Preplexity.
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Currently, Perplexity only supports "json_schema" method for structured output
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as per their [official documentation](https://docs.perplexity.ai/guides/structured-outputs).
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Args:
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schema: The output schema. Can be passed in as:
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- a JSON Schema,
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- a `TypedDict` class,
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- or a Pydantic class
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method: The method for steering model generation, currently only support:
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- `'json_schema'`: Use the JSON Schema to parse the model output
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include_raw:
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If `False` then only the parsed structured output is returned. If
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an error occurs during model output parsing it will be raised. If `True`
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then both the raw model response (a `BaseMessage`) and the parsed model
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response will be returned. If an error occurs during output parsing it
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will be caught and returned as well.
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The final output is always a `dict` with keys `'raw'`, `'parsed'`, and
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`'parsing_error'`.
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strict:
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Unsupported: whether to enable strict schema adherence when generating
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the output. This parameter is included for compatibility with other
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chat models, but is currently ignored.
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kwargs: Additional keyword args aren't supported.
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Returns:
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A `Runnable` that takes same inputs as a
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`langchain_core.language_models.chat.BaseChatModel`. If `include_raw` is
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`False` and `schema` is a Pydantic class, `Runnable` outputs an instance
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of `schema` (i.e., a Pydantic object). Otherwise, if `include_raw` is
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`False` then `Runnable` outputs a `dict`.
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If `include_raw` is `True`, then `Runnable` outputs a `dict` with keys:
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- `'raw'`: `BaseMessage`
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- `'parsed'`: `None` if there was a parsing error, otherwise the type
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depends on the `schema` as described above.
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- `'parsing_error'`: `BaseException | None`
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""" # noqa: E501
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if method in ("function_calling", "json_mode"):
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method = "json_schema"
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if method == "json_schema":
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if schema is None:
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raise ValueError(
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"schema must be specified when method is not 'json_schema'. "
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"Received None."
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)
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is_pydantic_schema = _is_pydantic_class(schema)
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response_format = convert_to_json_schema(schema)
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llm = self.bind(
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response_format={
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"type": "json_schema",
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"json_schema": {"schema": response_format},
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},
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ls_structured_output_format={
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"kwargs": {"method": method},
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"schema": response_format,
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},
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)
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output_parser = (
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PydanticOutputParser(pydantic_object=schema) # type: ignore[arg-type]
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if is_pydantic_schema
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else JsonOutputParser()
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)
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else:
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raise ValueError(
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f"Unrecognized method argument. Expected 'json_schema' Received:\
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'{method}'"
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)
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if include_raw:
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parser_assign = RunnablePassthrough.assign(
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parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
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)
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parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
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parser_with_fallback = parser_assign.with_fallbacks(
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[parser_none], exception_key="parsing_error"
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)
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return RunnableMap(raw=llm) | parser_with_fallback
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else:
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return llm | output_parser
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