Source code for langchain_community.utilities.google_trends

"""Util that calls Google Scholar Search."""
from typing import Any, Dict, Optional, cast

from langchain_core.pydantic_v1 import BaseModel, Extra, SecretStr, root_validator
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env

[docs]class GoogleTrendsAPIWrapper(BaseModel): """Wrapper for SerpApi's Google Scholar API You can create key by signing up at: The wrapper uses the python package: To use, you should have the environment variable ``SERPAPI_API_KEY`` set with your API key, or pass `serp_api_key` as a named parameter to the constructor. Example: .. code-block:: python from langchain_community.utilities import GoogleTrendsAPIWrapper google_trends = GoogleTrendsAPIWrapper()'langchain') """ serp_search_engine: Any serp_api_key: Optional[SecretStr] = None class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" values["serp_api_key"] = convert_to_secret_str( get_from_dict_or_env(values, "serp_api_key", "SERPAPI_API_KEY") ) try: from serpapi import SerpApiClient except ImportError: raise ImportError( "google-search-results is not installed. " "Please install it with `pip install google-search-results" ">=2.4.2`" ) serp_search_engine = SerpApiClient values["serp_search_engine"] = serp_search_engine return values
[docs] def run(self, query: str) -> str: """Run query through Google Trends with Serpapi""" serpapi_api_key = cast(SecretStr, self.serp_api_key) params = { "engine": "google_trends", "api_key": serpapi_api_key.get_secret_value(), "q": query, } total_results = [] client = self.serp_search_engine(params) client_dict = client.get_dict() total_results = ( client_dict["interest_over_time"]["timeline_data"] if "interest_over_time" in client_dict else None ) if not total_results: return "No good Trend Result was found" start_date = total_results[0]["date"].split() end_date = total_results[-1]["date"].split() values = [ results.get("values")[0].get("extracted_value") for results in total_results ] min_value = min(values) max_value = max(values) avg_value = sum(values) / len(values) percentage_change = ( (values[-1] - values[0]) / (values[0] if values[0] != 0 else 1) * (100 if values[0] != 0 else 1) ) params = { "engine": "google_trends", "api_key": serpapi_api_key.get_secret_value(), "data_type": "RELATED_QUERIES", "q": query, } total_results2 = {} client = self.serp_search_engine(params) total_results2 = client.get_dict().get("related_queries", {}) rising = [] top = [] rising = [results.get("query") for results in total_results2.get("rising", [])] top = [results.get("query") for results in total_results2.get("top", [])] doc = [ f"Query: {query}\n" f"Date From: {start_date[0]} {start_date[1]}, {start_date[-1]}\n" f"Date To: {end_date[0]} {end_date[3]} {end_date[-1]}\n" f"Min Value: {min_value}\n" f"Max Value: {max_value}\n" f"Average Value: {avg_value}\n" f"Percent Change: {str(percentage_change) + '%'}\n" f"Trend values: {', '.join([str(x) for x in values])}\n" f"Rising Related Queries: {', '.join(rising)}\n" f"Top Related Queries: {', '.join(top)}" ] return "\n\n".join(doc)