mirror of
https://github.com/hwchase17/langchain.git
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75823d580b
This pull request includes enhancements to the `perplexity.py` file in the `chat_models` module, focusing on improving the handling of additional keyword arguments (`additional_kwargs`) in message processing methods. Additionally, new unit tests have been added to ensure the correct inclusion of citations, images, and related questions in the `additional_kwargs`. Issue: resolves https://github.com/langchain-ai/langchain/issues/30439 Enhancements to `perplexity.py`: * [`libs/community/langchain_community/chat_models/perplexity.py`](diffhunk://#diff-d3e4d7b277608683913b53dcfdbd006f0f4a94d110d8b9ac7acf855f1f22207fL208-L212): Modified the `_convert_delta_to_message_chunk`, `_stream`, and `_generate` methods to handle `additional_kwargs`, which include citations, images, and related questions. [[1]](diffhunk://#diff-d3e4d7b277608683913b53dcfdbd006f0f4a94d110d8b9ac7acf855f1f22207fL208-L212) [[2]](diffhunk://#diff-d3e4d7b277608683913b53dcfdbd006f0f4a94d110d8b9ac7acf855f1f22207fL277-L286) [[3]](diffhunk://#diff-d3e4d7b277608683913b53dcfdbd006f0f4a94d110d8b9ac7acf855f1f22207fR324-R331) New unit tests: * [`libs/community/tests/unit_tests/chat_models/test_perplexity.py`](diffhunk://#diff-dab956d79bd7d17a0f5dea3f38ceab0d583b43b63eb1b29138ee9b6b271ba1d9R119-R275): Added new tests `test_perplexity_stream_includes_citations_and_images` and `test_perplexity_stream_includes_citations_and_related_questions` to verify that the `stream` method correctly includes citations, images, and related questions in the `additional_kwargs`.
520 lines
20 KiB
Python
520 lines
20 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 operator import itemgetter
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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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Literal,
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Mapping,
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Optional,
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Tuple,
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Type,
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TypeVar,
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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 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
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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 from_env, get_pydantic_field_names
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from langchain_core.utils.pydantic import (
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is_basemodel_subclass,
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)
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from pydantic import BaseModel, ConfigDict, Field, TypeAdapter, model_validator
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from typing_extensions import Self
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_BM = TypeVar("_BM", bound=BaseModel)
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_DictOrPydanticClass = Union[Dict[str, Any], Type[_BM], Type]
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_DictOrPydantic = Union[Dict, _BM]
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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 ``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
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can be passed in, even if not explicitly saved on this class.
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.. code-block:: bash
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pip install openai
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export PPLX_API_KEY=your_api_key
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Key init args - completion params:
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model: str
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Name of the model to use. e.g. "llama-3.1-sonar-small-128k-online"
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temperature: float
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Sampling temperature to use. Default is 0.7
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max_tokens: Optional[int]
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Maximum number of tokens to generate.
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streaming: bool
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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: Optional[str]
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API key for PerplexityChat API. Default is None.
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request_timeout: Optional[Union[float, Tuple[float, float]]]
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Timeout for requests to PerplexityChat completion API. Default is None.
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max_retries: int
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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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.. code-block:: python
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from langchain_community.chat_models import ChatPerplexity
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llm = ChatPerplexity(
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model="llama-3.1-sonar-small-128k-online",
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temperature=0.7,
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)
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Invoke:
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.. code-block:: python
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messages = [
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("system", "You are a chatbot."),
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("user", "Hello!")
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]
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llm.invoke(messages)
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Invoke with structured output:
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.. code-block:: 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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llm.with_structured_output(StructuredOutput)
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llm.invoke(messages)
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Invoke with perplexity-specific params:
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.. code-block:: python
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llm.invoke(messages, extra_body={"search_recency_filter": "week"})
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Stream:
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.. code-block:: python
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for chunk in llm.stream(messages):
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print(chunk.content)
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Token usage:
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.. code-block:: python
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response = llm.invoke(messages)
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response.usage_metadata
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Response metadata:
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.. code-block:: python
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response = llm.invoke(messages)
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response.response_metadata
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""" # noqa: E501
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client: Any = None #: :meta private:
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model: str = "llama-3.1-sonar-small-128k-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] = Field(
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default_factory=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: Optional[Union[float, Tuple[float, float]]] = Field(
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None, alias="timeout"
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)
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"""Timeout for requests to PerplexityChat completion API. Default is None."""
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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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model_config = ConfigDict(
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populate_by_name=True,
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)
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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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import openai
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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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self.client = openai.OpenAI(
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api_key=self.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 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: 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) # 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: 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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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: Optional[UsageMetadata] = None
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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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# 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: Optional[UsageMetadata] = {
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"input_tokens": lc_total_usage["input_tokens"]
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- prev_total_usage["input_tokens"],
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"output_tokens": lc_total_usage["output_tokens"]
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- prev_total_usage["output_tokens"],
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"total_tokens": lc_total_usage["total_tokens"]
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- prev_total_usage["total_tokens"],
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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"]:
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if attr in chunk:
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additional_kwargs[attr] = chunk[attr]
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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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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(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 = {"citations": response.citations}
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for attr in ["images", "related_questions"]:
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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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)
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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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"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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def with_structured_output(
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self,
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schema: Optional[_DictOrPydanticClass] = 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: Optional[bool] = 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, Preplexity 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:
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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. The final output is always a dict
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with keys "raw", "parsed", and "parsing_error".
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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 :class:`langchain_core.language_models.chat.BaseChatModel`.
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| If ``include_raw`` is False and ``schema`` is a Pydantic class, Runnable outputs an instance of ``schema`` (i.e., a Pydantic object). Otherwise, if ``include_raw`` is 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 depends on the ``schema`` as described above.
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- "parsing_error": Optional[BaseException]
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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(
|
|
"schema must be specified when method is not 'json_schema'. "
|
|
"Received None."
|
|
)
|
|
is_pydantic_schema = _is_pydantic_class(schema)
|
|
if is_pydantic_schema and hasattr(
|
|
schema, "model_json_schema"
|
|
): # accounting for pydantic v1 and v2
|
|
response_format = schema.model_json_schema() # type: ignore[union-attr]
|
|
elif is_pydantic_schema:
|
|
response_format = schema.schema() # type: ignore[union-attr]
|
|
elif isinstance(schema, dict):
|
|
response_format = schema
|
|
elif type(schema).__name__ == "_TypedDictMeta":
|
|
adapter = TypeAdapter(schema) # if use passes typeddict
|
|
response_format = adapter.json_schema()
|
|
|
|
llm = self.bind(
|
|
response_format={
|
|
"type": "json_schema",
|
|
"json_schema": {"schema": response_format},
|
|
}
|
|
)
|
|
output_parser = (
|
|
PydanticOutputParser(pydantic_object=schema) # type: ignore[arg-type]
|
|
if is_pydantic_schema
|
|
else JsonOutputParser()
|
|
)
|
|
else:
|
|
raise ValueError(
|
|
f"Unrecognized method argument. Expected 'json_schema' Received:\
|
|
'{method}'"
|
|
)
|
|
|
|
if include_raw:
|
|
parser_assign = RunnablePassthrough.assign(
|
|
parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
|
|
)
|
|
parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
|
|
parser_with_fallback = parser_assign.with_fallbacks(
|
|
[parser_none], exception_key="parsing_error"
|
|
)
|
|
return RunnableMap(raw=llm) | parser_with_fallback
|
|
else:
|
|
return llm | output_parser
|