langchain/libs/community/langchain_community/utilities/metaphor_search.py

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community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463) Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
2023-12-11 21:53:30 +00:00
"""Util that calls Metaphor Search API.
In order to set this up, follow instructions at:
"""
import json
from typing import Dict, List, Optional
import aiohttp
import requests
from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
from langchain_core.utils import get_from_dict_or_env
METAPHOR_API_URL = "https://api.metaphor.systems"
class MetaphorSearchAPIWrapper(BaseModel):
"""Wrapper for Metaphor Search API."""
metaphor_api_key: str
k: int = 10
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
def _metaphor_search_results(
self,
query: str,
num_results: int,
include_domains: Optional[List[str]] = None,
exclude_domains: Optional[List[str]] = None,
start_crawl_date: Optional[str] = None,
end_crawl_date: Optional[str] = None,
start_published_date: Optional[str] = None,
end_published_date: Optional[str] = None,
use_autoprompt: Optional[bool] = None,
) -> List[dict]:
headers = {"X-Api-Key": self.metaphor_api_key}
params = {
"numResults": num_results,
"query": query,
"includeDomains": include_domains,
"excludeDomains": exclude_domains,
"startCrawlDate": start_crawl_date,
"endCrawlDate": end_crawl_date,
"startPublishedDate": start_published_date,
"endPublishedDate": end_published_date,
"useAutoprompt": use_autoprompt,
}
response = requests.post(
# type: ignore
f"{METAPHOR_API_URL}/search",
headers=headers,
json=params,
)
response.raise_for_status()
search_results = response.json()
return search_results["results"]
@root_validator(pre=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and endpoint exists in environment."""
metaphor_api_key = get_from_dict_or_env(
values, "metaphor_api_key", "METAPHOR_API_KEY"
)
values["metaphor_api_key"] = metaphor_api_key
return values
def results(
self,
query: str,
num_results: int,
include_domains: Optional[List[str]] = None,
exclude_domains: Optional[List[str]] = None,
start_crawl_date: Optional[str] = None,
end_crawl_date: Optional[str] = None,
start_published_date: Optional[str] = None,
end_published_date: Optional[str] = None,
use_autoprompt: Optional[bool] = None,
) -> List[Dict]:
"""Run query through Metaphor Search and return metadata.
Args:
query: The query to search for.
num_results: The number of results to return.
include_domains: A list of domains to include in the search. Only one of include_domains and exclude_domains should be defined.
exclude_domains: A list of domains to exclude from the search. Only one of include_domains and exclude_domains should be defined.
start_crawl_date: If specified, only pages we crawled after start_crawl_date will be returned.
end_crawl_date: If specified, only pages we crawled before end_crawl_date will be returned.
start_published_date: If specified, only pages published after start_published_date will be returned.
end_published_date: If specified, only pages published before end_published_date will be returned.
use_autoprompt: If true, we turn your query into a more Metaphor-friendly query. Adds latency.
Returns:
A list of dictionaries with the following keys:
title - The title of the page
url - The url
author - Author of the content, if applicable. Otherwise, None.
published_date - Estimated date published
in YYYY-MM-DD format. Otherwise, None.
""" # noqa: E501
raw_search_results = self._metaphor_search_results(
query,
num_results=num_results,
include_domains=include_domains,
exclude_domains=exclude_domains,
start_crawl_date=start_crawl_date,
end_crawl_date=end_crawl_date,
start_published_date=start_published_date,
end_published_date=end_published_date,
use_autoprompt=use_autoprompt,
)
return self._clean_results(raw_search_results)
async def results_async(
self,
query: str,
num_results: int,
include_domains: Optional[List[str]] = None,
exclude_domains: Optional[List[str]] = None,
start_crawl_date: Optional[str] = None,
end_crawl_date: Optional[str] = None,
start_published_date: Optional[str] = None,
end_published_date: Optional[str] = None,
use_autoprompt: Optional[bool] = None,
) -> List[Dict]:
"""Get results from the Metaphor Search API asynchronously."""
# Function to perform the API call
async def fetch() -> str:
headers = {"X-Api-Key": self.metaphor_api_key}
params = {
"numResults": num_results,
"query": query,
"includeDomains": include_domains,
"excludeDomains": exclude_domains,
"startCrawlDate": start_crawl_date,
"endCrawlDate": end_crawl_date,
"startPublishedDate": start_published_date,
"endPublishedDate": end_published_date,
"useAutoprompt": use_autoprompt,
}
async with aiohttp.ClientSession() as session:
async with session.post(
f"{METAPHOR_API_URL}/search", json=params, headers=headers
) as res:
if res.status == 200:
data = await res.text()
return data
else:
raise Exception(f"Error {res.status}: {res.reason}")
results_json_str = await fetch()
results_json = json.loads(results_json_str)
return self._clean_results(results_json["results"])
def _clean_results(self, raw_search_results: List[Dict]) -> List[Dict]:
cleaned_results = []
for result in raw_search_results:
cleaned_results.append(
{
"title": result.get("title", "Unknown Title"),
"url": result.get("url", "Unknown URL"),
"author": result.get("author", "Unknown Author"),
"published_date": result.get("publishedDate", "Unknown Date"),
}
)
return cleaned_results