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https://github.com/hwchase17/langchain.git
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5396604ef4
- **Description:** Handing response where _interest_over_time_ is missing. - **Issue:** #15859 - **Dependencies:** None
122 lines
4.1 KiB
Python
122 lines
4.1 KiB
Python
"""Util that calls Google Scholar Search."""
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from typing import Any, Dict, Optional, cast
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from langchain_core.pydantic_v1 import BaseModel, Extra, SecretStr, root_validator
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from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
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class GoogleTrendsAPIWrapper(BaseModel):
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"""Wrapper for SerpApi's Google Scholar API
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You can create SerpApi.com key by signing up at: https://serpapi.com/users/sign_up.
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The wrapper uses the SerpApi.com python package:
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https://serpapi.com/integrations/python
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To use, you should have the environment variable ``SERPAPI_API_KEY``
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set with your API key, or pass `serp_api_key` as a named parameter
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to the constructor.
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Example:
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.. code-block:: python
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from langchain_community.utilities import GoogleTrendsAPIWrapper
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google_trends = GoogleTrendsAPIWrapper()
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google_trends.run('langchain')
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"""
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serp_search_engine: Any
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serp_api_key: Optional[SecretStr] = None
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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values["serp_api_key"] = convert_to_secret_str(
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get_from_dict_or_env(values, "serp_api_key", "SERPAPI_API_KEY")
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)
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try:
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from serpapi import SerpApiClient
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except ImportError:
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raise ImportError(
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"google-search-results is not installed. "
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"Please install it with `pip install google-search-results"
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">=2.4.2`"
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)
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serp_search_engine = SerpApiClient
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values["serp_search_engine"] = serp_search_engine
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return values
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def run(self, query: str) -> str:
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"""Run query through Google Trends with Serpapi"""
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serpapi_api_key = cast(SecretStr, self.serp_api_key)
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params = {
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"engine": "google_trends",
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"api_key": serpapi_api_key.get_secret_value(),
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"q": query,
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}
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total_results = []
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client = self.serp_search_engine(params)
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client_dict = client.get_dict()
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total_results = (
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client_dict["interest_over_time"]["timeline_data"]
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if "interest_over_time" in client_dict
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else None
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)
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if not total_results:
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return "No good Trend Result was found"
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start_date = total_results[0]["date"].split()
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end_date = total_results[-1]["date"].split()
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values = [
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results.get("values")[0].get("extracted_value") for results in total_results
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]
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min_value = min(values)
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max_value = max(values)
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avg_value = sum(values) / len(values)
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percentage_change = (
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(values[-1] - values[0])
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/ (values[0] if values[0] != 0 else 1)
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* (100 if values[0] != 0 else 1)
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)
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params = {
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"engine": "google_trends",
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"api_key": serpapi_api_key.get_secret_value(),
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"data_type": "RELATED_QUERIES",
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"q": query,
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}
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total_results2 = {}
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client = self.serp_search_engine(params)
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total_results2 = client.get_dict().get("related_queries", {})
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rising = []
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top = []
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rising = [results.get("query") for results in total_results2.get("rising", [])]
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top = [results.get("query") for results in total_results2.get("top", [])]
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doc = [
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f"Query: {query}\n"
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f"Date From: {start_date[0]} {start_date[1]}, {start_date[-1]}\n"
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f"Date To: {end_date[0]} {end_date[3]} {end_date[-1]}\n"
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f"Min Value: {min_value}\n"
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f"Max Value: {max_value}\n"
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f"Average Value: {avg_value}\n"
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f"Percent Change: {str(percentage_change) + '%'}\n"
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f"Trend values: {', '.join([str(x) for x in values])}\n"
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f"Rising Related Queries: {', '.join(rising)}\n"
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f"Top Related Queries: {', '.join(top)}"
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]
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return "\n\n".join(doc)
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