mirror of
https://github.com/hwchase17/langchain.git
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feat(perplexity): overhaul integration with official SDK and Search API (#34412)
This commit is contained in:
Kesku
GitHub
@@ -1,3 +1,27 @@
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from langchain_perplexity.chat_models import ChatPerplexity
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from langchain_perplexity.output_parsers import (
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ReasoningJsonOutputParser,
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ReasoningStructuredOutputParser,
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strip_think_tags,
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)
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from langchain_perplexity.retrievers import PerplexitySearchRetriever
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from langchain_perplexity.tools import PerplexitySearchResults
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from langchain_perplexity.types import (
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MediaResponse,
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MediaResponseOverrides,
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UserLocation,
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WebSearchOptions,
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)
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__all__ = ["ChatPerplexity"]
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__all__ = [
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"ChatPerplexity",
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"PerplexitySearchRetriever",
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"PerplexitySearchResults",
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"UserLocation",
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"WebSearchOptions",
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"MediaResponse",
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"MediaResponseOverrides",
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"ReasoningJsonOutputParser",
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"ReasoningStructuredOutputParser",
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"strip_think_tags",
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]
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25
libs/partners/perplexity/langchain_perplexity/_utils.py
Normal file
25
libs/partners/perplexity/langchain_perplexity/_utils.py
Normal file
@@ -0,0 +1,25 @@
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import os
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from typing import Any
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from langchain_core.utils import convert_to_secret_str
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from perplexity import Perplexity
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def initialize_client(values: dict[str, Any]) -> dict[str, Any]:
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"""Initialize the Perplexity client."""
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pplx_api_key = (
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values.get("pplx_api_key")
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or os.environ.get("PPLX_API_KEY")
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or os.environ.get("PERPLEXITY_API_KEY")
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or ""
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)
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values["pplx_api_key"] = convert_to_secret_str(pplx_api_key)
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api_key = (
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values["pplx_api_key"].get_secret_value() if values["pplx_api_key"] else None
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)
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if not values.get("client"):
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values["client"] = Perplexity(api_key=api_key)
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return values
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@@ -3,12 +3,14 @@
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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 collections.abc import AsyncIterator, Iterator, Mapping
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from operator import itemgetter
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from typing import Any, Literal, TypeAlias, cast
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import openai
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from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.callbacks import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models import (
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LanguageModelInput,
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ModelProfile,
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@@ -16,6 +18,7 @@ from langchain_core.language_models import (
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)
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from langchain_core.language_models.chat_models import (
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BaseChatModel,
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agenerate_from_stream,
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generate_from_stream,
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)
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from langchain_core.messages import (
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@@ -42,6 +45,7 @@ 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 perplexity import AsyncPerplexity, Perplexity
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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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@@ -50,6 +54,7 @@ from langchain_perplexity.output_parsers import (
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ReasoningJsonOutputParser,
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ReasoningStructuredOutputParser,
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)
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from langchain_perplexity.types import MediaResponse, WebSearchOptions
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_DictOrPydanticClass: TypeAlias = dict[str, Any] | type[BaseModel]
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_DictOrPydantic: TypeAlias = dict | BaseModel
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@@ -84,8 +89,10 @@ def _create_usage_metadata(token_usage: dict) -> UsageMetadata:
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# Build output_token_details for Perplexity-specific fields
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output_token_details: OutputTokenDetails = {}
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output_token_details["reasoning"] = token_usage.get("reasoning_tokens", 0)
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output_token_details["citation_tokens"] = token_usage.get("citation_tokens", 0) # type: ignore[typeddict-unknown-key]
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if (reasoning := token_usage.get("reasoning_tokens")) is not None:
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output_token_details["reasoning"] = reasoning
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if (citation_tokens := token_usage.get("citation_tokens")) is not None:
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output_token_details["citation_tokens"] = citation_tokens # type: ignore[typeddict-unknown-key]
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return UsageMetadata(
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input_tokens=input_tokens,
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@@ -100,7 +107,7 @@ class ChatPerplexity(BaseChatModel):
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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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Any parameters that are valid to be passed to the perplexity.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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@@ -157,12 +164,6 @@ class ChatPerplexity(BaseChatModel):
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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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@@ -182,7 +183,8 @@ class ChatPerplexity(BaseChatModel):
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```
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""" # noqa: E501
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client: Any = None
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client: Any = Field(default=None, exclude=True)
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async_client: Any = Field(default=None, exclude=True)
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model: str = "sonar"
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"""Model name."""
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@@ -196,8 +198,7 @@ class ChatPerplexity(BaseChatModel):
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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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"""Perplexity API key."""
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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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@@ -211,6 +212,51 @@ class ChatPerplexity(BaseChatModel):
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max_tokens: int | None = None
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"""Maximum number of tokens to generate."""
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search_mode: Literal["academic", "sec", "web"] | None = None
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"""Search mode for specialized content: "academic", "sec", or "web"."""
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reasoning_effort: Literal["low", "medium", "high"] | None = None
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"""Reasoning effort: "low", "medium", or "high" (default)."""
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language_preference: str | None = None
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"""Language preference:"""
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search_domain_filter: list[str] | None = None
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"""Search domain filter: list of domains to filter search results (max 20)."""
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return_images: bool = False
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"""Whether to return images in the response."""
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return_related_questions: bool = False
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"""Whether to return related questions in the response."""
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search_recency_filter: Literal["day", "week", "month", "year"] | None = None
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"""Filter search results by recency: "day", "week", "month", or "year"."""
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search_after_date_filter: str | None = None
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"""Search after date filter: date in format "MM/DD/YYYY" (default)."""
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search_before_date_filter: str | None = None
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"""Only return results before this date (format: MM/DD/YYYY)."""
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last_updated_after_filter: str | None = None
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"""Only return results updated after this date (format: MM/DD/YYYY)."""
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last_updated_before_filter: str | None = None
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"""Only return results updated before this date (format: MM/DD/YYYY)."""
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disable_search: bool = False
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"""Whether to disable web search entirely."""
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enable_search_classifier: bool = False
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"""Whether to enable the search classifier."""
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web_search_options: WebSearchOptions | None = None
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"""Configuration for web search behavior including Pro Search."""
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media_response: MediaResponse | None = None
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"""Media response: "images", "videos", or "none" (default)."""
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model_config = ConfigDict(populate_by_name=True)
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@property
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@@ -247,19 +293,16 @@ class ChatPerplexity(BaseChatModel):
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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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pplx_api_key = (
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self.pplx_api_key.get_secret_value() if self.pplx_api_key else None
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)
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if not self.client:
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self.client = Perplexity(api_key=pplx_api_key)
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if not self.async_client:
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self.async_client = AsyncPerplexity(api_key=pplx_api_key)
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return self
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@model_validator(mode="after")
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@@ -272,12 +315,49 @@ class ChatPerplexity(BaseChatModel):
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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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params: dict[str, Any] = {
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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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if self.search_mode:
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params["search_mode"] = self.search_mode
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if self.reasoning_effort:
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params["reasoning_effort"] = self.reasoning_effort
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if self.language_preference:
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params["language_preference"] = self.language_preference
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if self.search_domain_filter:
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params["search_domain_filter"] = self.search_domain_filter
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if self.return_images:
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params["return_images"] = self.return_images
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if self.return_related_questions:
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params["return_related_questions"] = self.return_related_questions
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if self.search_recency_filter:
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params["search_recency_filter"] = self.search_recency_filter
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if self.search_after_date_filter:
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params["search_after_date_filter"] = self.search_after_date_filter
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if self.search_before_date_filter:
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params["search_before_date_filter"] = self.search_before_date_filter
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if self.last_updated_after_filter:
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params["last_updated_after_filter"] = self.last_updated_after_filter
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if self.last_updated_before_filter:
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params["last_updated_before_filter"] = self.last_updated_before_filter
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if self.disable_search:
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params["disable_search"] = self.disable_search
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if self.enable_search_classifier:
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params["enable_search_classifier"] = self.enable_search_classifier
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if self.web_search_options:
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params["web_search_options"] = self.web_search_options.model_dump(
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exclude_none=True
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)
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if self.media_response:
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if "extra_body" not in params:
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params["extra_body"] = {}
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params["extra_body"]["media_response"] = self.media_response.model_dump(
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exclude_none=True
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)
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return {**params, **self.model_kwargs}
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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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@@ -353,6 +433,7 @@ class ChatPerplexity(BaseChatModel):
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added_model_name: bool = False
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added_search_queries: bool = False
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added_search_context_size: 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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@@ -379,17 +460,26 @@ class ChatPerplexity(BaseChatModel):
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if attr in chunk:
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additional_kwargs[attr] = chunk[attr]
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if chunk.get("videos"):
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additional_kwargs["videos"] = chunk["videos"]
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if chunk.get("reasoning_steps"):
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additional_kwargs["reasoning_steps"] = chunk["reasoning_steps"]
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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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# Add num_search_queries to generation_info if present
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if total_usage := chunk.get("usage"):
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if num_search_queries := total_usage.get("num_search_queries"):
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if not added_search_queries:
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generation_info["num_search_queries"] = num_search_queries
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added_search_queries = True
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if not added_search_context_size:
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if search_context_size := total_usage.get("search_context_size"):
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generation_info["search_context_size"] = search_context_size
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added_search_context_size = 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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@@ -411,6 +501,91 @@ class ChatPerplexity(BaseChatModel):
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run_manager.on_llm_new_token(chunk.text, chunk=chunk)
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yield chunk
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async def _astream(
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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: AsyncCallbackManagerForLLMRun | None = None,
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**kwargs: Any,
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) -> AsyncIterator[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 = await self.async_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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added_search_queries: bool = False
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async 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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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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|
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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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|
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if chunk.get("videos"):
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additional_kwargs["videos"] = chunk["videos"]
|
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|
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if chunk.get("reasoning_steps"):
|
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additional_kwargs["reasoning_steps"] = chunk["reasoning_steps"]
|
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|
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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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|
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if total_usage := chunk.get("usage"):
|
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if num_search_queries := total_usage.get("num_search_queries"):
|
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if not added_search_queries:
|
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generation_info["num_search_queries"] = num_search_queries
|
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added_search_queries = True
|
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if search_context_size := total_usage.get("search_context_size"):
|
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generation_info["search_context_size"] = search_context_size
|
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|
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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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|
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if isinstance(chunk, AIMessageChunk) and usage_metadata:
|
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chunk.usage_metadata = usage_metadata
|
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|
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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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|
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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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|
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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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await run_manager.on_llm_new_token(chunk.text, chunk=chunk)
|
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yield chunk
|
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|
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def _generate(
|
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self,
|
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messages: list[BaseMessage],
|
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@@ -427,8 +602,9 @@ class ChatPerplexity(BaseChatModel):
|
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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_dict = usage.model_dump()
|
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|
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if hasattr(response, "usage") and response.usage:
|
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usage_dict = response.usage.model_dump()
|
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usage_metadata = _create_usage_metadata(usage_dict)
|
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else:
|
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usage_metadata = None
|
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@@ -436,15 +612,87 @@ class ChatPerplexity(BaseChatModel):
|
||||
|
||||
additional_kwargs = {}
|
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for attr in ["citations", "images", "related_questions", "search_results"]:
|
||||
if hasattr(response, attr):
|
||||
if hasattr(response, attr) and getattr(response, attr):
|
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additional_kwargs[attr] = getattr(response, attr)
|
||||
|
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# Build response_metadata with model_name and num_search_queries
|
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if hasattr(response, "videos") and response.videos:
|
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additional_kwargs["videos"] = [
|
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v.model_dump() if hasattr(v, "model_dump") else v
|
||||
for v in response.videos
|
||||
]
|
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|
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if hasattr(response, "reasoning_steps") and response.reasoning_steps:
|
||||
additional_kwargs["reasoning_steps"] = [
|
||||
r.model_dump() if hasattr(r, "model_dump") else r
|
||||
for r in response.reasoning_steps
|
||||
]
|
||||
|
||||
response_metadata: dict[str, Any] = {
|
||||
"model_name": getattr(response, "model", self.model)
|
||||
}
|
||||
if num_search_queries := usage_dict.get("num_search_queries"):
|
||||
response_metadata["num_search_queries"] = num_search_queries
|
||||
if search_context_size := usage_dict.get("search_context_size"):
|
||||
response_metadata["search_context_size"] = search_context_size
|
||||
|
||||
message = AIMessage(
|
||||
content=response.choices[0].message.content,
|
||||
additional_kwargs=additional_kwargs,
|
||||
usage_metadata=usage_metadata,
|
||||
response_metadata=response_metadata,
|
||||
)
|
||||
return ChatResult(generations=[ChatGeneration(message=message)])
|
||||
|
||||
async def _agenerate(
|
||||
self,
|
||||
messages: list[BaseMessage],
|
||||
stop: list[str] | None = None,
|
||||
run_manager: AsyncCallbackManagerForLLMRun | None = None,
|
||||
**kwargs: Any,
|
||||
) -> ChatResult:
|
||||
if self.streaming:
|
||||
stream_iter = self._astream(
|
||||
messages, stop=stop, run_manager=run_manager, **kwargs
|
||||
)
|
||||
if stream_iter:
|
||||
return await agenerate_from_stream(stream_iter)
|
||||
message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
params = {**params, **kwargs}
|
||||
response = await self.async_client.chat.completions.create(
|
||||
messages=message_dicts, **params
|
||||
)
|
||||
|
||||
if hasattr(response, "usage") and response.usage:
|
||||
usage_dict = response.usage.model_dump()
|
||||
usage_metadata = _create_usage_metadata(usage_dict)
|
||||
else:
|
||||
usage_metadata = None
|
||||
usage_dict = {}
|
||||
|
||||
additional_kwargs = {}
|
||||
for attr in ["citations", "images", "related_questions", "search_results"]:
|
||||
if hasattr(response, attr) and getattr(response, attr):
|
||||
additional_kwargs[attr] = getattr(response, attr)
|
||||
|
||||
if hasattr(response, "videos") and response.videos:
|
||||
additional_kwargs["videos"] = [
|
||||
v.model_dump() if hasattr(v, "model_dump") else v
|
||||
for v in response.videos
|
||||
]
|
||||
|
||||
if hasattr(response, "reasoning_steps") and response.reasoning_steps:
|
||||
additional_kwargs["reasoning_steps"] = [
|
||||
r.model_dump() if hasattr(r, "model_dump") else r
|
||||
for r in response.reasoning_steps
|
||||
]
|
||||
|
||||
response_metadata: dict[str, Any] = {
|
||||
"model_name": getattr(response, "model", self.model)
|
||||
}
|
||||
if num_search_queries := usage_dict.get("num_search_queries"):
|
||||
response_metadata["num_search_queries"] = num_search_queries
|
||||
if search_context_size := usage_dict.get("search_context_size"):
|
||||
response_metadata["search_context_size"] = search_context_size
|
||||
|
||||
message = AIMessage(
|
||||
content=response.choices[0].message.content,
|
||||
|
||||
@@ -64,4 +64,16 @@ _PROFILES: dict[str, dict[str, Any]] = {
|
||||
"reasoning_output": True,
|
||||
"tool_calling": False,
|
||||
},
|
||||
"sonar-deep-research": {
|
||||
"max_input_tokens": 128000,
|
||||
"max_output_tokens": 8192,
|
||||
"image_inputs": True,
|
||||
"audio_inputs": False,
|
||||
"video_inputs": False,
|
||||
"image_outputs": False,
|
||||
"audio_outputs": False,
|
||||
"video_outputs": False,
|
||||
"reasoning_output": True,
|
||||
"tool_calling": False,
|
||||
},
|
||||
}
|
||||
|
||||
@@ -0,0 +1,13 @@
|
||||
provider = "perplexity"
|
||||
|
||||
[overrides."sonar-deep-research"]
|
||||
max_input_tokens = 128000
|
||||
max_output_tokens = 8192
|
||||
image_inputs = true
|
||||
audio_inputs = false
|
||||
video_inputs = false
|
||||
image_outputs = false
|
||||
audio_outputs = false
|
||||
video_outputs = false
|
||||
reasoning_output = true
|
||||
tool_calling = false
|
||||
68
libs/partners/perplexity/langchain_perplexity/retrievers.py
Normal file
68
libs/partners/perplexity/langchain_perplexity/retrievers.py
Normal file
@@ -0,0 +1,68 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any, Literal
|
||||
|
||||
from langchain_core.callbacks import CallbackManagerForRetrieverRun
|
||||
from langchain_core.documents import Document
|
||||
from langchain_core.retrievers import BaseRetriever
|
||||
from pydantic import Field, SecretStr, model_validator
|
||||
|
||||
from langchain_perplexity._utils import initialize_client
|
||||
|
||||
|
||||
class PerplexitySearchRetriever(BaseRetriever):
|
||||
"""Perplexity Search retriever."""
|
||||
|
||||
k: int = Field(default=10, description="Max results (1-20)")
|
||||
max_tokens: int = Field(default=25000, description="Max tokens across all results")
|
||||
max_tokens_per_page: int = Field(default=1024, description="Max tokens per page")
|
||||
country: str | None = Field(default=None, description="ISO country code")
|
||||
search_domain_filter: list[str] | None = Field(
|
||||
default=None, description="Domain filter (max 20)"
|
||||
)
|
||||
search_recency_filter: Literal["day", "week", "month", "year"] | None = None
|
||||
search_after_date: str | None = Field(
|
||||
default=None, description="Date filter (format: %m/%d/%Y)"
|
||||
)
|
||||
search_before_date: str | None = Field(
|
||||
default=None, description="Date filter (format: %m/%d/%Y)"
|
||||
)
|
||||
|
||||
client: Any = Field(default=None, exclude=True)
|
||||
pplx_api_key: SecretStr = Field(default=SecretStr(""))
|
||||
|
||||
@model_validator(mode="before")
|
||||
@classmethod
|
||||
def validate_environment(cls, values: dict) -> Any:
|
||||
"""Validate the environment."""
|
||||
return initialize_client(values)
|
||||
|
||||
def _get_relevant_documents(
|
||||
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
|
||||
) -> list[Document]:
|
||||
params = {
|
||||
"query": query,
|
||||
"max_results": self.k,
|
||||
"max_tokens": self.max_tokens,
|
||||
"max_tokens_per_page": self.max_tokens_per_page,
|
||||
"country": self.country,
|
||||
"search_domain_filter": self.search_domain_filter,
|
||||
"search_recency_filter": self.search_recency_filter,
|
||||
"search_after_date": self.search_after_date,
|
||||
"search_before_date": self.search_before_date,
|
||||
}
|
||||
params = {k: v for k, v in params.items() if v is not None}
|
||||
response = self.client.search.create(**params)
|
||||
|
||||
return [
|
||||
Document(
|
||||
page_content=result.snippet,
|
||||
metadata={
|
||||
"title": result.title,
|
||||
"url": result.url,
|
||||
"date": result.date,
|
||||
"last_updated": result.last_updated,
|
||||
},
|
||||
)
|
||||
for result in response.results
|
||||
]
|
||||
66
libs/partners/perplexity/langchain_perplexity/tools.py
Normal file
66
libs/partners/perplexity/langchain_perplexity/tools.py
Normal file
@@ -0,0 +1,66 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any, Literal
|
||||
|
||||
from langchain_core.callbacks import CallbackManagerForToolRun
|
||||
from langchain_core.tools import BaseTool
|
||||
from pydantic import Field, SecretStr, model_validator
|
||||
|
||||
from langchain_perplexity._utils import initialize_client
|
||||
|
||||
|
||||
class PerplexitySearchResults(BaseTool):
|
||||
"""Perplexity Search tool."""
|
||||
|
||||
name: str = "perplexity_search_results_json"
|
||||
description: str = (
|
||||
"A wrapper around Perplexity Search. "
|
||||
"Input should be a search query. "
|
||||
"Output is a JSON array of the query results"
|
||||
)
|
||||
client: Any = Field(default=None, exclude=True)
|
||||
pplx_api_key: SecretStr = Field(default=SecretStr(""))
|
||||
|
||||
@model_validator(mode="before")
|
||||
@classmethod
|
||||
def validate_environment(cls, values: dict) -> Any:
|
||||
"""Validate the environment."""
|
||||
return initialize_client(values)
|
||||
|
||||
def _run(
|
||||
self,
|
||||
query: str | list[str],
|
||||
max_results: int = 10,
|
||||
country: str | None = None,
|
||||
search_domain_filter: list[str] | None = None,
|
||||
search_recency_filter: Literal["day", "week", "month", "year"] | None = None,
|
||||
search_after_date: str | None = None,
|
||||
search_before_date: str | None = None,
|
||||
run_manager: CallbackManagerForToolRun | None = None,
|
||||
) -> list[dict] | str:
|
||||
"""Use the tool."""
|
||||
try:
|
||||
params = {
|
||||
"query": query,
|
||||
"max_results": max_results,
|
||||
"country": country,
|
||||
"search_domain_filter": search_domain_filter,
|
||||
"search_recency_filter": search_recency_filter,
|
||||
"search_after_date": search_after_date,
|
||||
"search_before_date": search_before_date,
|
||||
}
|
||||
params = {k: v for k, v in params.items() if v is not None}
|
||||
response = self.client.search.create(**params)
|
||||
return [
|
||||
{
|
||||
"title": result.title,
|
||||
"url": result.url,
|
||||
"snippet": result.snippet,
|
||||
"date": result.date,
|
||||
"last_updated": result.last_updated,
|
||||
}
|
||||
for result in response.results
|
||||
]
|
||||
except Exception as e:
|
||||
msg = f"Perplexity search failed: {type(e).__name__}"
|
||||
return msg
|
||||
29
libs/partners/perplexity/langchain_perplexity/types.py
Normal file
29
libs/partners/perplexity/langchain_perplexity/types.py
Normal file
@@ -0,0 +1,29 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Literal
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
|
||||
class UserLocation(BaseModel):
|
||||
latitude: float | None = None
|
||||
longitude: float | None = None
|
||||
country: str | None = None
|
||||
region: str | None = None
|
||||
city: str | None = None
|
||||
|
||||
|
||||
class WebSearchOptions(BaseModel):
|
||||
search_context_size: Literal["low", "medium", "high"] | None = None
|
||||
user_location: UserLocation | None = None
|
||||
search_type: Literal["fast", "pro", "auto"] | None = None
|
||||
image_search_relevance_enhanced: bool | None = None
|
||||
|
||||
|
||||
class MediaResponseOverrides(BaseModel):
|
||||
return_videos: bool | None = None
|
||||
return_images: bool | None = None
|
||||
|
||||
|
||||
class MediaResponse(BaseModel):
|
||||
overrides: MediaResponseOverrides | None = None
|
||||
@@ -13,7 +13,7 @@ version = "1.1.0"
|
||||
requires-python = ">=3.10.0,<4.0.0"
|
||||
dependencies = [
|
||||
"langchain-core>=1.1.0,<2.0.0",
|
||||
"openai>=2.0.0,<3.0.0",
|
||||
"perplexityai>=0.22.0",
|
||||
]
|
||||
|
||||
[project.urls]
|
||||
|
||||
@@ -0,0 +1,125 @@
|
||||
"""Integration tests for ChatPerplexity."""
|
||||
|
||||
import os
|
||||
|
||||
import pytest
|
||||
from langchain_core.messages import HumanMessage
|
||||
|
||||
from langchain_perplexity import ChatPerplexity, MediaResponse, WebSearchOptions
|
||||
|
||||
|
||||
@pytest.mark.skipif(not os.environ.get("PPLX_API_KEY"), reason="PPLX_API_KEY not set")
|
||||
class TestChatPerplexityIntegration:
|
||||
def test_standard_generation(self) -> None:
|
||||
"""Test standard generation."""
|
||||
chat = ChatPerplexity(model="sonar", temperature=0)
|
||||
message = HumanMessage(content="Hello! How are you?")
|
||||
response = chat.invoke([message])
|
||||
assert response.content
|
||||
assert isinstance(response.content, str)
|
||||
|
||||
async def test_async_generation(self) -> None:
|
||||
"""Test async generation."""
|
||||
chat = ChatPerplexity(model="sonar", temperature=0)
|
||||
message = HumanMessage(content="Hello! How are you?")
|
||||
response = await chat.ainvoke([message])
|
||||
assert response.content
|
||||
assert isinstance(response.content, str)
|
||||
|
||||
def test_pro_search(self) -> None:
|
||||
"""Test Pro Search (reasoning_steps extraction)."""
|
||||
# Pro search is available on sonar-pro
|
||||
chat = ChatPerplexity(
|
||||
model="sonar-pro",
|
||||
temperature=0,
|
||||
web_search_options=WebSearchOptions(search_type="pro"),
|
||||
streaming=True,
|
||||
)
|
||||
message = HumanMessage(content="Who won the 2024 US election and why?")
|
||||
|
||||
# We need to collect chunks to check reasoning steps
|
||||
chunks = list(chat.stream([message]))
|
||||
full_content = "".join(c.content for c in chunks if isinstance(c.content, str))
|
||||
assert full_content
|
||||
|
||||
# Check if any chunk has reasoning_steps
|
||||
has_reasoning = any("reasoning_steps" in c.additional_kwargs for c in chunks)
|
||||
if has_reasoning:
|
||||
assert True
|
||||
else:
|
||||
# Fallback assertion if no reasoning steps returned
|
||||
assert len(chunks) > 0
|
||||
|
||||
async def test_streaming(self) -> None:
|
||||
"""Test streaming."""
|
||||
chat = ChatPerplexity(model="sonar", temperature=0)
|
||||
message = HumanMessage(content="Count to 5")
|
||||
async for chunk in chat.astream([message]):
|
||||
assert isinstance(chunk.content, str)
|
||||
|
||||
def test_citations_and_search_results(self) -> None:
|
||||
"""Test that citations and search results are returned."""
|
||||
chat = ChatPerplexity(model="sonar", temperature=0)
|
||||
message = HumanMessage(content="Who is the CEO of OpenAI?")
|
||||
response = chat.invoke([message])
|
||||
|
||||
# Citations are usually in additional_kwargs
|
||||
assert "citations" in response.additional_kwargs
|
||||
# Search results might be there too
|
||||
# Note: presence depends on whether search was performed
|
||||
if response.additional_kwargs.get("citations"):
|
||||
assert len(response.additional_kwargs["citations"]) > 0
|
||||
|
||||
def test_search_control(self) -> None:
|
||||
"""Test search control parameters."""
|
||||
# Test disabled search (should complete without citations)
|
||||
chat = ChatPerplexity(model="sonar", disable_search=True)
|
||||
message = HumanMessage(content="What is 2+2?")
|
||||
response = chat.invoke([message])
|
||||
assert response.content
|
||||
|
||||
# Test search classifier
|
||||
chat_classifier = ChatPerplexity(model="sonar", enable_search_classifier=True)
|
||||
response_classifier = chat_classifier.invoke([message])
|
||||
assert response_classifier.content
|
||||
|
||||
def test_search_recency_filter(self) -> None:
|
||||
"""Test search_recency_filter parameter."""
|
||||
chat = ChatPerplexity(model="sonar", search_recency_filter="month")
|
||||
message = HumanMessage(content="Latest AI news")
|
||||
response = chat.invoke([message])
|
||||
assert response.content
|
||||
|
||||
def test_search_domain_filter(self) -> None:
|
||||
"""Test search_domain_filter parameter."""
|
||||
chat = ChatPerplexity(model="sonar", search_domain_filter=["wikipedia.org"])
|
||||
message = HumanMessage(content="Python programming language")
|
||||
response = chat.invoke([message])
|
||||
|
||||
# Verify citations come from wikipedia if any
|
||||
if citations := response.additional_kwargs.get("citations"):
|
||||
assert any("wikipedia.org" in c for c in citations)
|
||||
|
||||
def test_media_and_metadata(self) -> None:
|
||||
"""Test related questions and images."""
|
||||
chat = ChatPerplexity(
|
||||
model="sonar-pro",
|
||||
return_related_questions=True,
|
||||
return_images=True,
|
||||
# Media response overrides for video
|
||||
media_response=MediaResponse(overrides={"return_videos": True}),
|
||||
)
|
||||
message = HumanMessage(content="Apollo 11 moon landing")
|
||||
response = chat.invoke([message])
|
||||
|
||||
# Check related questions
|
||||
if related := response.additional_kwargs.get("related_questions"):
|
||||
assert len(related) > 0
|
||||
|
||||
# Check images
|
||||
if images := response.additional_kwargs.get("images"):
|
||||
assert len(images) > 0
|
||||
|
||||
# Check videos (might not always be present but structure should handle it)
|
||||
if videos := response.additional_kwargs.get("videos"):
|
||||
assert len(videos) > 0
|
||||
@@ -0,0 +1,56 @@
|
||||
"""Integration tests for Perplexity Search API."""
|
||||
|
||||
import os
|
||||
|
||||
import pytest
|
||||
from langchain_core.documents import Document
|
||||
|
||||
from langchain_perplexity import PerplexitySearchResults, PerplexitySearchRetriever
|
||||
|
||||
|
||||
@pytest.mark.skipif(not os.environ.get("PPLX_API_KEY"), reason="PPLX_API_KEY not set")
|
||||
class TestPerplexitySearchAPI:
|
||||
def test_search_retriever_basic(self) -> None:
|
||||
"""Test basic search with retriever."""
|
||||
retriever = PerplexitySearchRetriever(k=3)
|
||||
docs = retriever.invoke("What is the capital of France?")
|
||||
assert len(docs) > 0
|
||||
assert isinstance(docs[0], Document)
|
||||
assert "Paris" in docs[0].page_content
|
||||
assert docs[0].metadata["title"]
|
||||
assert docs[0].metadata["url"]
|
||||
|
||||
def test_search_retriever_with_filters(self) -> None:
|
||||
"""Test search with filters."""
|
||||
# Search for recent news (recency filter)
|
||||
retriever = PerplexitySearchRetriever(
|
||||
k=3, search_recency_filter="month", search_domain_filter=["wikipedia.org"]
|
||||
)
|
||||
docs = retriever.invoke("Python programming language")
|
||||
assert len(docs) > 0
|
||||
for doc in docs:
|
||||
assert "wikipedia.org" in doc.metadata["url"]
|
||||
|
||||
def test_search_tool_basic(self) -> None:
|
||||
"""Test basic search with tool."""
|
||||
tool = PerplexitySearchResults(max_results=3)
|
||||
results = tool.invoke("Who won the 2024 Super Bowl?")
|
||||
|
||||
# BaseTool.invoke calls _run. If return_direct is False (default),
|
||||
# it returns the output of _run, which is a list of dicts.
|
||||
assert isinstance(results, list)
|
||||
assert len(results) > 0
|
||||
assert "title" in results[0]
|
||||
assert "url" in results[0]
|
||||
assert "snippet" in results[0]
|
||||
|
||||
def test_search_tool_multi_query(self) -> None:
|
||||
"""Test search tool with multiple queries."""
|
||||
tool = PerplexitySearchResults(max_results=2)
|
||||
queries = ["Apple stock price", "Microsoft stock price"]
|
||||
# Pass input as dict to avoid BaseTool validation error with list
|
||||
results = tool.invoke({"query": queries})
|
||||
|
||||
assert isinstance(results, list)
|
||||
# Should have results for both (combined)
|
||||
assert len(results) > 0
|
||||
@@ -4,7 +4,7 @@ from unittest.mock import MagicMock
|
||||
from langchain_core.messages import AIMessageChunk, BaseMessage
|
||||
from pytest_mock import MockerFixture
|
||||
|
||||
from langchain_perplexity import ChatPerplexity
|
||||
from langchain_perplexity import ChatPerplexity, MediaResponse, WebSearchOptions
|
||||
from langchain_perplexity.chat_models import _create_usage_metadata
|
||||
|
||||
|
||||
@@ -41,6 +41,32 @@ def test_perplexity_initialization() -> None:
|
||||
)
|
||||
|
||||
|
||||
def test_perplexity_new_params() -> None:
|
||||
"""Test new Perplexity-specific parameters."""
|
||||
web_search_options = WebSearchOptions(search_type="pro", search_context_size="high")
|
||||
media_response = MediaResponse(overrides={"return_videos": True})
|
||||
|
||||
llm = ChatPerplexity(
|
||||
model="sonar-pro",
|
||||
search_mode="academic",
|
||||
web_search_options=web_search_options,
|
||||
media_response=media_response,
|
||||
return_images=True,
|
||||
)
|
||||
|
||||
params = llm._default_params
|
||||
assert params["search_mode"] == "academic"
|
||||
assert params["web_search_options"] == {
|
||||
"search_type": "pro",
|
||||
"search_context_size": "high",
|
||||
}
|
||||
|
||||
assert params["extra_body"]["media_response"] == {
|
||||
"overrides": {"return_videos": True}
|
||||
}
|
||||
assert params["return_images"] is True
|
||||
|
||||
|
||||
def test_perplexity_stream_includes_citations(mocker: MockerFixture) -> None:
|
||||
"""Test that the stream method includes citations in the additional_kwargs."""
|
||||
llm = ChatPerplexity(model="test", timeout=30, verbose=True)
|
||||
@@ -88,214 +114,34 @@ def test_perplexity_stream_includes_citations(mocker: MockerFixture) -> None:
|
||||
patcher.assert_called_once()
|
||||
|
||||
|
||||
def test_perplexity_stream_includes_citations_and_images(mocker: MockerFixture) -> None:
|
||||
"""Test that the stream method includes citations in the additional_kwargs."""
|
||||
llm = ChatPerplexity(model="test", timeout=30, verbose=True)
|
||||
mock_chunk_0 = {
|
||||
"choices": [{"delta": {"content": "Hello "}, "finish_reason": None}],
|
||||
"citations": ["example.com", "example2.com"],
|
||||
"images": [
|
||||
{
|
||||
"image_url": "mock_image_url",
|
||||
"origin_url": "mock_origin_url",
|
||||
"height": 100,
|
||||
"width": 100,
|
||||
}
|
||||
],
|
||||
}
|
||||
mock_chunk_1 = {
|
||||
"choices": [{"delta": {"content": "Perplexity"}, "finish_reason": None}],
|
||||
"citations": ["example.com", "example2.com"],
|
||||
"images": [
|
||||
{
|
||||
"image_url": "mock_image_url",
|
||||
"origin_url": "mock_origin_url",
|
||||
"height": 100,
|
||||
"width": 100,
|
||||
}
|
||||
],
|
||||
}
|
||||
mock_chunk_2 = {
|
||||
"choices": [{"delta": {}, "finish_reason": "stop"}],
|
||||
}
|
||||
mock_chunks: list[dict[str, Any]] = [mock_chunk_0, mock_chunk_1, mock_chunk_2]
|
||||
mock_stream = MagicMock()
|
||||
mock_stream.__iter__.return_value = mock_chunks
|
||||
patcher = mocker.patch.object(
|
||||
llm.client.chat.completions, "create", return_value=mock_stream
|
||||
)
|
||||
stream = llm.stream("Hello langchain")
|
||||
full: BaseMessage | None = None
|
||||
chunks_list = list(stream)
|
||||
# BaseChatModel.stream() adds an extra chunk after the final chunk from _stream
|
||||
assert len(chunks_list) == 4
|
||||
for i, chunk in enumerate(
|
||||
chunks_list[:3]
|
||||
): # Only check first 3 chunks against mock
|
||||
full = chunk if full is None else cast(BaseMessage, full + chunk)
|
||||
assert chunk.content == mock_chunks[i]["choices"][0]["delta"].get("content", "")
|
||||
if i == 0:
|
||||
assert chunk.additional_kwargs["citations"] == [
|
||||
"example.com",
|
||||
"example2.com",
|
||||
]
|
||||
assert chunk.additional_kwargs["images"] == [
|
||||
{
|
||||
"image_url": "mock_image_url",
|
||||
"origin_url": "mock_origin_url",
|
||||
"height": 100,
|
||||
"width": 100,
|
||||
}
|
||||
]
|
||||
else:
|
||||
assert "citations" not in chunk.additional_kwargs
|
||||
assert "images" not in chunk.additional_kwargs
|
||||
# Process the 4th chunk
|
||||
assert full is not None
|
||||
full = cast(BaseMessage, full + chunks_list[3])
|
||||
assert isinstance(full, AIMessageChunk)
|
||||
assert full.content == "Hello Perplexity"
|
||||
assert full.additional_kwargs == {
|
||||
"citations": ["example.com", "example2.com"],
|
||||
"images": [
|
||||
{
|
||||
"image_url": "mock_image_url",
|
||||
"origin_url": "mock_origin_url",
|
||||
"height": 100,
|
||||
"width": 100,
|
||||
}
|
||||
],
|
||||
}
|
||||
|
||||
patcher.assert_called_once()
|
||||
|
||||
|
||||
def test_perplexity_stream_includes_citations_and_related_questions(
|
||||
mocker: MockerFixture,
|
||||
) -> None:
|
||||
"""Test that the stream method includes citations in the additional_kwargs."""
|
||||
llm = ChatPerplexity(model="test", timeout=30, verbose=True)
|
||||
mock_chunk_0 = {
|
||||
"choices": [{"delta": {"content": "Hello "}, "finish_reason": None}],
|
||||
"citations": ["example.com", "example2.com"],
|
||||
"related_questions": ["example_question_1", "example_question_2"],
|
||||
}
|
||||
mock_chunk_1 = {
|
||||
"choices": [{"delta": {"content": "Perplexity"}, "finish_reason": None}],
|
||||
"citations": ["example.com", "example2.com"],
|
||||
"related_questions": ["example_question_1", "example_question_2"],
|
||||
}
|
||||
mock_chunk_2 = {
|
||||
"choices": [{"delta": {}, "finish_reason": "stop"}],
|
||||
}
|
||||
mock_chunks: list[dict[str, Any]] = [mock_chunk_0, mock_chunk_1, mock_chunk_2]
|
||||
mock_stream = MagicMock()
|
||||
mock_stream.__iter__.return_value = mock_chunks
|
||||
patcher = mocker.patch.object(
|
||||
llm.client.chat.completions, "create", return_value=mock_stream
|
||||
)
|
||||
stream = llm.stream("Hello langchain")
|
||||
full: BaseMessage | None = None
|
||||
chunks_list = list(stream)
|
||||
# BaseChatModel.stream() adds an extra chunk after the final chunk from _stream
|
||||
assert len(chunks_list) == 4
|
||||
for i, chunk in enumerate(
|
||||
chunks_list[:3]
|
||||
): # Only check first 3 chunks against mock
|
||||
full = chunk if full is None else cast(BaseMessage, full + chunk)
|
||||
assert chunk.content == mock_chunks[i]["choices"][0]["delta"].get("content", "")
|
||||
if i == 0:
|
||||
assert chunk.additional_kwargs["citations"] == [
|
||||
"example.com",
|
||||
"example2.com",
|
||||
]
|
||||
assert chunk.additional_kwargs["related_questions"] == [
|
||||
"example_question_1",
|
||||
"example_question_2",
|
||||
]
|
||||
else:
|
||||
assert "citations" not in chunk.additional_kwargs
|
||||
assert "related_questions" not in chunk.additional_kwargs
|
||||
# Process the 4th chunk
|
||||
assert full is not None
|
||||
full = cast(BaseMessage, full + chunks_list[3])
|
||||
assert isinstance(full, AIMessageChunk)
|
||||
assert full.content == "Hello Perplexity"
|
||||
assert full.additional_kwargs == {
|
||||
"citations": ["example.com", "example2.com"],
|
||||
"related_questions": ["example_question_1", "example_question_2"],
|
||||
}
|
||||
|
||||
patcher.assert_called_once()
|
||||
|
||||
|
||||
def test_perplexity_stream_includes_citations_and_search_results(
|
||||
mocker: MockerFixture,
|
||||
) -> None:
|
||||
"""Test that the stream method exposes `search_results` via additional_kwargs."""
|
||||
def test_perplexity_stream_includes_videos_and_reasoning(mocker: MockerFixture) -> None:
|
||||
"""Test that stream extracts videos and reasoning_steps."""
|
||||
llm = ChatPerplexity(model="test", timeout=30, verbose=True)
|
||||
|
||||
mock_chunk_0 = {
|
||||
"choices": [{"delta": {"content": "Hello "}, "finish_reason": None}],
|
||||
"citations": ["example.com/a", "example.com/b"],
|
||||
"search_results": [
|
||||
{"title": "Mock result", "url": "https://example.com/result", "date": None}
|
||||
],
|
||||
"choices": [{"delta": {"content": "Thinking... "}, "finish_reason": None}],
|
||||
"videos": [{"url": "http://video.com", "thumbnail_url": "http://thumb.com"}],
|
||||
"reasoning_steps": [{"thought": "I should search", "type": "web_search"}],
|
||||
}
|
||||
mock_chunk_1 = {
|
||||
"choices": [{"delta": {"content": "Perplexity"}, "finish_reason": None}],
|
||||
"citations": ["example.com/a", "example.com/b"],
|
||||
"search_results": [
|
||||
{"title": "Mock result", "url": "https://example.com/result", "date": None}
|
||||
],
|
||||
}
|
||||
mock_chunk_2 = {
|
||||
"choices": [{"delta": {}, "finish_reason": "stop"}],
|
||||
}
|
||||
mock_chunks: list[dict[str, Any]] = [mock_chunk_0, mock_chunk_1, mock_chunk_2]
|
||||
|
||||
mock_chunks: list[dict[str, Any]] = [mock_chunk_0, mock_chunk_1]
|
||||
mock_stream = MagicMock()
|
||||
mock_stream.__iter__.return_value = mock_chunks
|
||||
patcher = mocker.patch.object(
|
||||
llm.client.chat.completions, "create", return_value=mock_stream
|
||||
)
|
||||
stream = llm.stream("Hello langchain")
|
||||
full: BaseMessage | None = None
|
||||
chunks_list = list(stream)
|
||||
# BaseChatModel.stream() adds an extra chunk after the final chunk from _stream
|
||||
assert len(chunks_list) == 4
|
||||
for i, chunk in enumerate(
|
||||
chunks_list[:3]
|
||||
): # Only check first 3 chunks against mock
|
||||
full = chunk if full is None else cast(BaseMessage, full + chunk)
|
||||
assert chunk.content == mock_chunks[i]["choices"][0]["delta"].get("content", "")
|
||||
if i == 0:
|
||||
assert chunk.additional_kwargs["citations"] == [
|
||||
"example.com/a",
|
||||
"example.com/b",
|
||||
]
|
||||
assert chunk.additional_kwargs["search_results"] == [
|
||||
{
|
||||
"title": "Mock result",
|
||||
"url": "https://example.com/result",
|
||||
"date": None,
|
||||
}
|
||||
]
|
||||
else:
|
||||
assert "citations" not in chunk.additional_kwargs
|
||||
assert "search_results" not in chunk.additional_kwargs
|
||||
# Process the 4th chunk
|
||||
assert full is not None
|
||||
full = cast(BaseMessage, full + chunks_list[3])
|
||||
assert isinstance(full, AIMessageChunk)
|
||||
assert full.content == "Hello Perplexity"
|
||||
assert full.additional_kwargs == {
|
||||
"citations": ["example.com/a", "example.com/b"],
|
||||
"search_results": [
|
||||
{"title": "Mock result", "url": "https://example.com/result", "date": None}
|
||||
],
|
||||
}
|
||||
mocker.patch.object(llm.client.chat.completions, "create", return_value=mock_stream)
|
||||
|
||||
patcher.assert_called_once()
|
||||
stream = list(llm.stream("test"))
|
||||
first_chunk = stream[0]
|
||||
|
||||
assert "videos" in first_chunk.additional_kwargs
|
||||
assert first_chunk.additional_kwargs["videos"][0]["url"] == "http://video.com"
|
||||
assert "reasoning_steps" in first_chunk.additional_kwargs
|
||||
assert (
|
||||
first_chunk.additional_kwargs["reasoning_steps"][0]["thought"]
|
||||
== "I should search"
|
||||
)
|
||||
|
||||
|
||||
def test_create_usage_metadata_basic() -> None:
|
||||
@@ -304,6 +150,8 @@ def test_create_usage_metadata_basic() -> None:
|
||||
"prompt_tokens": 10,
|
||||
"completion_tokens": 20,
|
||||
"total_tokens": 30,
|
||||
"reasoning_tokens": 0,
|
||||
"citation_tokens": 0,
|
||||
}
|
||||
|
||||
usage_metadata = _create_usage_metadata(token_usage)
|
||||
@@ -315,94 +163,6 @@ def test_create_usage_metadata_basic() -> None:
|
||||
assert usage_metadata["output_token_details"]["citation_tokens"] == 0 # type: ignore[typeddict-item]
|
||||
|
||||
|
||||
def test_create_usage_metadata_with_reasoning_tokens() -> None:
|
||||
"""Test _create_usage_metadata with reasoning tokens."""
|
||||
token_usage = {
|
||||
"prompt_tokens": 50,
|
||||
"completion_tokens": 100,
|
||||
"total_tokens": 150,
|
||||
"reasoning_tokens": 25,
|
||||
}
|
||||
|
||||
usage_metadata = _create_usage_metadata(token_usage)
|
||||
|
||||
assert usage_metadata["input_tokens"] == 50
|
||||
assert usage_metadata["output_tokens"] == 100
|
||||
assert usage_metadata["total_tokens"] == 150
|
||||
assert usage_metadata["output_token_details"]["reasoning"] == 25
|
||||
assert usage_metadata["output_token_details"]["citation_tokens"] == 0 # type: ignore[typeddict-item]
|
||||
|
||||
|
||||
def test_create_usage_metadata_with_citation_tokens() -> None:
|
||||
"""Test _create_usage_metadata with citation tokens."""
|
||||
token_usage = {
|
||||
"prompt_tokens": 100,
|
||||
"completion_tokens": 200,
|
||||
"total_tokens": 300,
|
||||
"citation_tokens": 15,
|
||||
}
|
||||
|
||||
usage_metadata = _create_usage_metadata(token_usage)
|
||||
|
||||
assert usage_metadata["input_tokens"] == 100
|
||||
assert usage_metadata["output_tokens"] == 200
|
||||
assert usage_metadata["total_tokens"] == 300
|
||||
assert usage_metadata["output_token_details"]["reasoning"] == 0
|
||||
assert usage_metadata["output_token_details"]["citation_tokens"] == 15 # type: ignore[typeddict-item]
|
||||
|
||||
|
||||
def test_create_usage_metadata_with_all_token_types() -> None:
|
||||
"""Test _create_usage_metadata with all token types.
|
||||
|
||||
Tests reasoning tokens and citation tokens together.
|
||||
"""
|
||||
token_usage = {
|
||||
"prompt_tokens": 75,
|
||||
"completion_tokens": 150,
|
||||
"total_tokens": 225,
|
||||
"reasoning_tokens": 30,
|
||||
"citation_tokens": 20,
|
||||
}
|
||||
|
||||
usage_metadata = _create_usage_metadata(token_usage)
|
||||
|
||||
assert usage_metadata["input_tokens"] == 75
|
||||
assert usage_metadata["output_tokens"] == 150
|
||||
assert usage_metadata["total_tokens"] == 225
|
||||
assert usage_metadata["output_token_details"]["reasoning"] == 30
|
||||
assert usage_metadata["output_token_details"]["citation_tokens"] == 20 # type: ignore[typeddict-item]
|
||||
|
||||
|
||||
def test_create_usage_metadata_missing_optional_fields() -> None:
|
||||
"""Test _create_usage_metadata with missing optional fields defaults to 0."""
|
||||
token_usage = {
|
||||
"prompt_tokens": 25,
|
||||
"completion_tokens": 50,
|
||||
}
|
||||
|
||||
usage_metadata = _create_usage_metadata(token_usage)
|
||||
|
||||
assert usage_metadata["input_tokens"] == 25
|
||||
assert usage_metadata["output_tokens"] == 50
|
||||
# Total tokens should be calculated if not provided
|
||||
assert usage_metadata["total_tokens"] == 75
|
||||
assert usage_metadata["output_token_details"]["reasoning"] == 0
|
||||
assert usage_metadata["output_token_details"]["citation_tokens"] == 0 # type: ignore[typeddict-item]
|
||||
|
||||
|
||||
def test_create_usage_metadata_empty_dict() -> None:
|
||||
"""Test _create_usage_metadata with empty token usage dict."""
|
||||
token_usage: dict = {}
|
||||
|
||||
usage_metadata = _create_usage_metadata(token_usage)
|
||||
|
||||
assert usage_metadata["input_tokens"] == 0
|
||||
assert usage_metadata["output_tokens"] == 0
|
||||
assert usage_metadata["total_tokens"] == 0
|
||||
assert usage_metadata["output_token_details"]["reasoning"] == 0
|
||||
assert usage_metadata["output_token_details"]["citation_tokens"] == 0 # type: ignore[typeddict-item]
|
||||
|
||||
|
||||
def test_perplexity_invoke_includes_num_search_queries(mocker: MockerFixture) -> None:
|
||||
"""Test that invoke includes num_search_queries in response_metadata."""
|
||||
llm = ChatPerplexity(model="test", timeout=30, verbose=True)
|
||||
@@ -413,6 +173,7 @@ def test_perplexity_invoke_includes_num_search_queries(mocker: MockerFixture) ->
|
||||
"completion_tokens": 20,
|
||||
"total_tokens": 30,
|
||||
"num_search_queries": 3,
|
||||
"search_context_size": "high",
|
||||
}
|
||||
|
||||
mock_response = MagicMock()
|
||||
@@ -427,6 +188,13 @@ def test_perplexity_invoke_includes_num_search_queries(mocker: MockerFixture) ->
|
||||
]
|
||||
mock_response.model = "test-model"
|
||||
mock_response.usage = mock_usage
|
||||
# Mock optional fields as empty/None
|
||||
mock_response.videos = None
|
||||
mock_response.reasoning_steps = None
|
||||
mock_response.citations = None
|
||||
mock_response.search_results = None
|
||||
mock_response.images = None
|
||||
mock_response.related_questions = None
|
||||
|
||||
patcher = mocker.patch.object(
|
||||
llm.client.chat.completions, "create", return_value=mock_response
|
||||
@@ -435,96 +203,11 @@ def test_perplexity_invoke_includes_num_search_queries(mocker: MockerFixture) ->
|
||||
result = llm.invoke("Test query")
|
||||
|
||||
assert result.response_metadata["num_search_queries"] == 3
|
||||
assert result.response_metadata["search_context_size"] == "high"
|
||||
assert result.response_metadata["model_name"] == "test-model"
|
||||
patcher.assert_called_once()
|
||||
|
||||
|
||||
def test_perplexity_invoke_without_num_search_queries(mocker: MockerFixture) -> None:
|
||||
"""Test that invoke works when num_search_queries is not provided."""
|
||||
llm = ChatPerplexity(model="test", timeout=30, verbose=True)
|
||||
|
||||
mock_usage = MagicMock()
|
||||
mock_usage.model_dump.return_value = {
|
||||
"prompt_tokens": 10,
|
||||
"completion_tokens": 20,
|
||||
"total_tokens": 30,
|
||||
}
|
||||
|
||||
mock_response = MagicMock()
|
||||
mock_response.choices = [
|
||||
MagicMock(
|
||||
message=MagicMock(
|
||||
content="Test response",
|
||||
tool_calls=None,
|
||||
),
|
||||
finish_reason="stop",
|
||||
)
|
||||
]
|
||||
mock_response.model = "test-model"
|
||||
mock_response.usage = mock_usage
|
||||
|
||||
patcher = mocker.patch.object(
|
||||
llm.client.chat.completions, "create", return_value=mock_response
|
||||
)
|
||||
|
||||
result = llm.invoke("Test query")
|
||||
|
||||
assert "num_search_queries" not in result.response_metadata
|
||||
assert result.response_metadata["model_name"] == "test-model"
|
||||
patcher.assert_called_once()
|
||||
|
||||
|
||||
def test_perplexity_stream_includes_num_search_queries(mocker: MockerFixture) -> None:
|
||||
"""Test that stream properly handles num_search_queries in usage data."""
|
||||
llm = ChatPerplexity(model="test", timeout=30, verbose=True)
|
||||
|
||||
mock_chunk_0 = {
|
||||
"choices": [{"delta": {"content": "Hello "}, "finish_reason": None}],
|
||||
}
|
||||
mock_chunk_1 = {
|
||||
"choices": [{"delta": {"content": "world"}, "finish_reason": None}],
|
||||
}
|
||||
mock_chunk_2 = {
|
||||
"choices": [{"delta": {}, "finish_reason": "stop"}],
|
||||
"usage": {
|
||||
"prompt_tokens": 5,
|
||||
"completion_tokens": 10,
|
||||
"total_tokens": 15,
|
||||
"num_search_queries": 2,
|
||||
"reasoning_tokens": 1,
|
||||
"citation_tokens": 3,
|
||||
},
|
||||
}
|
||||
mock_chunks: list[dict[str, Any]] = [mock_chunk_0, mock_chunk_1, mock_chunk_2]
|
||||
mock_stream = MagicMock()
|
||||
mock_stream.__iter__.return_value = mock_chunks
|
||||
|
||||
patcher = mocker.patch.object(
|
||||
llm.client.chat.completions, "create", return_value=mock_stream
|
||||
)
|
||||
|
||||
chunks_list = list(llm.stream("Test query"))
|
||||
|
||||
# Find the chunk with usage metadata
|
||||
usage_chunk = None
|
||||
for chunk in chunks_list:
|
||||
if chunk.usage_metadata:
|
||||
usage_chunk = chunk
|
||||
break
|
||||
|
||||
# Verify usage metadata is properly set
|
||||
assert usage_chunk is not None
|
||||
assert usage_chunk.usage_metadata is not None
|
||||
assert usage_chunk.usage_metadata["input_tokens"] == 5
|
||||
assert usage_chunk.usage_metadata["output_tokens"] == 10
|
||||
assert usage_chunk.usage_metadata["total_tokens"] == 15
|
||||
# Verify reasoning and citation tokens are included
|
||||
assert usage_chunk.usage_metadata["output_token_details"]["reasoning"] == 1
|
||||
assert usage_chunk.usage_metadata["output_token_details"]["citation_tokens"] == 3 # type: ignore[typeddict-item]
|
||||
|
||||
patcher.assert_called_once()
|
||||
|
||||
|
||||
def test_profile() -> None:
|
||||
model = ChatPerplexity(model="sonar")
|
||||
assert model.profile
|
||||
|
||||
@@ -1,6 +1,17 @@
|
||||
from langchain_perplexity import __all__
|
||||
|
||||
EXPECTED_ALL = ["ChatPerplexity"]
|
||||
EXPECTED_ALL = [
|
||||
"ChatPerplexity",
|
||||
"PerplexitySearchRetriever",
|
||||
"PerplexitySearchResults",
|
||||
"UserLocation",
|
||||
"WebSearchOptions",
|
||||
"MediaResponse",
|
||||
"MediaResponseOverrides",
|
||||
"ReasoningJsonOutputParser",
|
||||
"ReasoningStructuredOutputParser",
|
||||
"strip_think_tags",
|
||||
]
|
||||
|
||||
|
||||
def test_all_imports() -> None:
|
||||
|
||||
42
libs/partners/perplexity/tests/unit_tests/test_retrievers.py
Normal file
42
libs/partners/perplexity/tests/unit_tests/test_retrievers.py
Normal file
@@ -0,0 +1,42 @@
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
from pytest_mock import MockerFixture
|
||||
|
||||
from langchain_perplexity import PerplexitySearchRetriever
|
||||
|
||||
|
||||
def test_search_retriever_initialization() -> None:
|
||||
retriever = PerplexitySearchRetriever(pplx_api_key="test")
|
||||
assert retriever.pplx_api_key.get_secret_value() == "test"
|
||||
assert retriever.k == 10
|
||||
|
||||
|
||||
def test_search_retriever_get_relevant_documents(mocker: MockerFixture) -> None:
|
||||
retriever = PerplexitySearchRetriever(pplx_api_key="test")
|
||||
|
||||
mock_result = MagicMock()
|
||||
mock_result.title = "Test Title"
|
||||
mock_result.url = "http://test.com"
|
||||
mock_result.snippet = "Test snippet"
|
||||
mock_result.date = "2023-01-01"
|
||||
mock_result.last_updated = "2023-01-02"
|
||||
|
||||
mock_response = MagicMock()
|
||||
mock_response.results = [mock_result]
|
||||
|
||||
mock_create = MagicMock(return_value=mock_response)
|
||||
mocker.patch.object(retriever.client.search, "create", mock_create)
|
||||
|
||||
docs = retriever.invoke("query")
|
||||
|
||||
assert len(docs) == 1
|
||||
assert docs[0].page_content == "Test snippet"
|
||||
assert docs[0].metadata["title"] == "Test Title"
|
||||
assert docs[0].metadata["url"] == "http://test.com"
|
||||
|
||||
mock_create.assert_called_once_with(
|
||||
query="query",
|
||||
max_results=10,
|
||||
max_tokens=25000,
|
||||
max_tokens_per_page=1024,
|
||||
)
|
||||
35
libs/partners/perplexity/tests/unit_tests/test_tools.py
Normal file
35
libs/partners/perplexity/tests/unit_tests/test_tools.py
Normal file
@@ -0,0 +1,35 @@
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
from pytest_mock import MockerFixture
|
||||
|
||||
from langchain_perplexity import PerplexitySearchResults
|
||||
|
||||
|
||||
def test_search_tool_run(mocker: MockerFixture) -> None:
|
||||
tool = PerplexitySearchResults(pplx_api_key="test")
|
||||
|
||||
mock_result = MagicMock()
|
||||
mock_result.title = "Test Title"
|
||||
mock_result.url = "http://test.com"
|
||||
mock_result.snippet = "Test snippet"
|
||||
mock_result.date = "2023-01-01"
|
||||
mock_result.last_updated = "2023-01-02"
|
||||
|
||||
mock_response = MagicMock()
|
||||
mock_response.results = [mock_result]
|
||||
|
||||
mock_create = MagicMock(return_value=mock_response)
|
||||
mocker.patch.object(tool.client.search, "create", mock_create)
|
||||
|
||||
result = tool.invoke("query")
|
||||
|
||||
# result should be a list of dicts (converted by tool) or str if string output
|
||||
# By default, tool.invoke returns the output of _run.
|
||||
assert isinstance(result, list)
|
||||
assert len(result) == 1
|
||||
assert result[0]["title"] == "Test Title"
|
||||
|
||||
mock_create.assert_called_once_with(
|
||||
query="query",
|
||||
max_results=10,
|
||||
)
|
||||
129
libs/partners/perplexity/uv.lock
generated
129
libs/partners/perplexity/uv.lock
generated
@@ -399,79 +399,6 @@ wheels = [
|
||||
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|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "jiter"
|
||||
version = "0.11.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/9d/c0/a3bb4cc13aced219dd18191ea66e874266bd8aa7b96744e495e1c733aa2d/jiter-0.11.0.tar.gz", hash = "sha256:1d9637eaf8c1d6a63d6562f2a6e5ab3af946c66037eb1b894e8fad75422266e4", size = 167094, upload-time = "2025-09-15T09:20:38.212Z" }
|
||||
wheels = [
|
||||
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||||
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{ name = "pydantic" },
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{ name = "sniffio" },
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{ name = "typing-extensions" },
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wheels = [
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]
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[[package]]
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@@ -1861,18 +1786,6 @@ wheels = [
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[[package]]
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wheels = [
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]
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|
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[[package]]
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name = "types-requests"
|
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version = "2.31.0.6"
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|
||||
Reference in New Issue
Block a user