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"""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
class GoogleTrendsAPIWrapper(BaseModel):
"""Wrapper for SerpApi's Google Scholar API
You can create SerpApi.com key by signing up at: https://serpapi.com/users/sign_up.
The wrapper uses the SerpApi.com python package:
https://serpapi.com/integrations/python
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()
google_trends.run('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
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)
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