mirror of
https://github.com/hwchase17/langchain.git
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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
This commit is contained in:
171
libs/community/langchain_community/utilities/metaphor_search.py
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171
libs/community/langchain_community/utilities/metaphor_search.py
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"""Util that calls Metaphor Search API.
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In order to set this up, follow instructions at:
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"""
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import json
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from typing import Dict, List, Optional
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import aiohttp
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import requests
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from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
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from langchain_core.utils import get_from_dict_or_env
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METAPHOR_API_URL = "https://api.metaphor.systems"
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class MetaphorSearchAPIWrapper(BaseModel):
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"""Wrapper for Metaphor Search API."""
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metaphor_api_key: str
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k: int = 10
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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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def _metaphor_search_results(
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self,
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query: str,
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num_results: int,
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include_domains: Optional[List[str]] = None,
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exclude_domains: Optional[List[str]] = None,
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start_crawl_date: Optional[str] = None,
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end_crawl_date: Optional[str] = None,
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start_published_date: Optional[str] = None,
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end_published_date: Optional[str] = None,
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use_autoprompt: Optional[bool] = None,
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) -> List[dict]:
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headers = {"X-Api-Key": self.metaphor_api_key}
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params = {
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"numResults": num_results,
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"query": query,
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"includeDomains": include_domains,
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"excludeDomains": exclude_domains,
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"startCrawlDate": start_crawl_date,
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"endCrawlDate": end_crawl_date,
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"startPublishedDate": start_published_date,
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"endPublishedDate": end_published_date,
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"useAutoprompt": use_autoprompt,
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}
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response = requests.post(
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# type: ignore
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f"{METAPHOR_API_URL}/search",
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headers=headers,
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json=params,
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)
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response.raise_for_status()
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search_results = response.json()
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return search_results["results"]
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@root_validator(pre=True)
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and endpoint exists in environment."""
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metaphor_api_key = get_from_dict_or_env(
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values, "metaphor_api_key", "METAPHOR_API_KEY"
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)
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values["metaphor_api_key"] = metaphor_api_key
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return values
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def results(
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self,
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query: str,
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num_results: int,
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include_domains: Optional[List[str]] = None,
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exclude_domains: Optional[List[str]] = None,
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start_crawl_date: Optional[str] = None,
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end_crawl_date: Optional[str] = None,
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start_published_date: Optional[str] = None,
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end_published_date: Optional[str] = None,
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use_autoprompt: Optional[bool] = None,
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) -> List[Dict]:
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"""Run query through Metaphor Search and return metadata.
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Args:
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query: The query to search for.
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num_results: The number of results to return.
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include_domains: A list of domains to include in the search. Only one of include_domains and exclude_domains should be defined.
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exclude_domains: A list of domains to exclude from the search. Only one of include_domains and exclude_domains should be defined.
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start_crawl_date: If specified, only pages we crawled after start_crawl_date will be returned.
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end_crawl_date: If specified, only pages we crawled before end_crawl_date will be returned.
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start_published_date: If specified, only pages published after start_published_date will be returned.
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end_published_date: If specified, only pages published before end_published_date will be returned.
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use_autoprompt: If true, we turn your query into a more Metaphor-friendly query. Adds latency.
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Returns:
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A list of dictionaries with the following keys:
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title - The title of the page
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url - The url
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author - Author of the content, if applicable. Otherwise, None.
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published_date - Estimated date published
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in YYYY-MM-DD format. Otherwise, None.
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""" # noqa: E501
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raw_search_results = self._metaphor_search_results(
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query,
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num_results=num_results,
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include_domains=include_domains,
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exclude_domains=exclude_domains,
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start_crawl_date=start_crawl_date,
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end_crawl_date=end_crawl_date,
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start_published_date=start_published_date,
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end_published_date=end_published_date,
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use_autoprompt=use_autoprompt,
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)
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return self._clean_results(raw_search_results)
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async def results_async(
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self,
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query: str,
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num_results: int,
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include_domains: Optional[List[str]] = None,
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exclude_domains: Optional[List[str]] = None,
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start_crawl_date: Optional[str] = None,
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end_crawl_date: Optional[str] = None,
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start_published_date: Optional[str] = None,
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end_published_date: Optional[str] = None,
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use_autoprompt: Optional[bool] = None,
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) -> List[Dict]:
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"""Get results from the Metaphor Search API asynchronously."""
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# Function to perform the API call
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async def fetch() -> str:
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headers = {"X-Api-Key": self.metaphor_api_key}
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params = {
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"numResults": num_results,
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"query": query,
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"includeDomains": include_domains,
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"excludeDomains": exclude_domains,
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"startCrawlDate": start_crawl_date,
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"endCrawlDate": end_crawl_date,
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"startPublishedDate": start_published_date,
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"endPublishedDate": end_published_date,
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"useAutoprompt": use_autoprompt,
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}
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async with aiohttp.ClientSession() as session:
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async with session.post(
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f"{METAPHOR_API_URL}/search", json=params, headers=headers
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) as res:
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if res.status == 200:
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data = await res.text()
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return data
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else:
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raise Exception(f"Error {res.status}: {res.reason}")
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results_json_str = await fetch()
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results_json = json.loads(results_json_str)
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return self._clean_results(results_json["results"])
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def _clean_results(self, raw_search_results: List[Dict]) -> List[Dict]:
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cleaned_results = []
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for result in raw_search_results:
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cleaned_results.append(
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{
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"title": result.get("title", "Unknown Title"),
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"url": result.get("url", "Unknown URL"),
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"author": result.get("author", "Unknown Author"),
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"published_date": result.get("publishedDate", "Unknown Date"),
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}
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)
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return cleaned_results
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