Source code for langchain_community.utilities.google_scholar

"""Util that calls Google Scholar Search."""

from typing import Any, Dict, Optional

from langchain_core.utils import get_from_dict_or_env
from pydantic import BaseModel, ConfigDict, model_validator



[docs]
class GoogleScholarAPIWrapper(BaseModel):
    """Wrapper for Google Scholar API

    You can create serpapi key by signing up at: https://serpapi.com/users/sign_up.

    The wrapper uses the serpapi python package:
    https://serpapi.com/integrations/python#search-google-scholar

    To use, you should have the environment variable ``SERP_API_KEY``
    set with your API key, or pass `serp_api_key` as a named parameter
    to the constructor.

    Attributes:
        top_k_results: number of results to return from google-scholar query search.
            By default it returns top 10 results.
        hl: attribute defines the language to use for the Google Scholar search.
            It's a two-letter language code.
            (e.g., en for English, es for Spanish, or fr for French). Head to the
            Google languages page for a full list of supported Google languages:
            https://serpapi.com/google-languages

        lr: attribute defines one or multiple languages to limit the search to.
            It uses lang_{two-letter language code} to specify languages
            and | as a delimiter. (e.g., lang_fr|lang_de will only search French
            and German pages). Head to the Google lr languages for a full
            list of supported languages: https://serpapi.com/google-lr-languages

     Example:
        .. code-block:: python

        from langchain_community.utilities import GoogleScholarAPIWrapper
        google_scholar = GoogleScholarAPIWrapper()
        google_scholar.run('langchain')
    """

    top_k_results: int = 10
    hl: str = "en"
    lr: str = "lang_en"
    serp_api_key: Optional[str] = None
    google_scholar_engine: Any = None

    model_config = ConfigDict(
        extra="forbid",
    )

    @model_validator(mode="before")
    @classmethod
    def validate_environment(cls, values: Dict) -> Any:
        """Validate that api key and python package exists in environment."""
        serp_api_key = get_from_dict_or_env(values, "serp_api_key", "SERP_API_KEY")
        values["serp_api_key"] = serp_api_key

        try:
            from serpapi import GoogleScholarSearch

        except ImportError:
            raise ImportError(
                "google-search-results is not installed. "
                "Please install it with `pip install google-search-results"
                ">=2.4.2`"
            )
        GoogleScholarSearch.SERP_API_KEY = serp_api_key
        values["google_scholar_engine"] = GoogleScholarSearch

        return values


[docs]
    def run(self, query: str) -> str:
        """Run query through GoogleSearchScholar and parse result"""
        total_results = []
        page = 0
        while page < max((self.top_k_results - 20), 1):
            # We are getting 20 results from every page
            # which is the max in order to reduce the number of API CALLS.
            # 0 is the first page of results, 20 is the 2nd page of results,
            # 40 is the 3rd page of results, etc.
            results = (
                self.google_scholar_engine(  # type: ignore
                    {
                        "q": query,
                        "start": page,
                        "hl": self.hl,
                        "num": min(
                            self.top_k_results, 20
                        ),  # if top_k_result is less than 20.
                        "lr": self.lr,
                    }
                )
                .get_dict()
                .get("organic_results", [])
            )
            total_results.extend(results)
            if not results:  # No need to search for more pages if current page
                # has returned no results
                break
            page += 20
        if (
            self.top_k_results % 20 != 0 and page > 20 and total_results
        ):  # From the last page we would only need top_k_results%20 results
            # if k is not divisible by 20.
            results = (
                self.google_scholar_engine(  # type: ignore
                    {
                        "q": query,
                        "start": page,
                        "num": self.top_k_results % 20,
                        "hl": self.hl,
                        "lr": self.lr,
                    }
                )
                .get_dict()
                .get("organic_results", [])
            )
            total_results.extend(results)
        if not total_results:
            return "No good Google Scholar Result was found"
        docs = [
            f"Title: {result.get('title', '')}\n"
            f"Authors: {','.join([author.get('name') for author in result.get('publication_info', {}).get('authors', [])])}\n"  # noqa: E501
            f"Summary: {result.get('publication_info', {}).get('summary', '')}\n"
            f"Total-Citations: {result.get('inline_links', {}).get('cited_by', {}).get('total', '')}"  # noqa: E501
            for result in total_results
        ]
        return "\n\n".join(docs)