mirror of
https://github.com/hwchase17/langchain
synced 2024-11-04 06:00:26 +00:00
ed58eeb9c5
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
54 lines
1.7 KiB
Python
54 lines
1.7 KiB
Python
"""
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Adapted from https://github.com/venuv/langchain_yt_tools
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CustomYTSearchTool searches YouTube videos related to a person
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and returns a specified number of video URLs.
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Input to this tool should be a comma separated list,
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- the first part contains a person name
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- and the second(optional) a number that is the
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maximum number of video results to return
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"""
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import json
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from typing import Optional
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from langchain_core.callbacks import CallbackManagerForToolRun
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from langchain_community.tools import BaseTool
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class YouTubeSearchTool(BaseTool):
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"""Tool that queries YouTube."""
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name: str = "youtube_search"
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description: str = (
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"search for youtube videos associated with a person. "
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"the input to this tool should be a comma separated list, "
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"the first part contains a person name and the second a "
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"number that is the maximum number of video results "
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"to return aka num_results. the second part is optional"
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)
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def _search(self, person: str, num_results: int) -> str:
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from youtube_search import YoutubeSearch
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results = YoutubeSearch(person, num_results).to_json()
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data = json.loads(results)
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url_suffix_list = [
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"https://www.youtube.com" + video["url_suffix"] for video in data["videos"]
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]
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return str(url_suffix_list)
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def _run(
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self,
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query: str,
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run_manager: Optional[CallbackManagerForToolRun] = None,
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) -> str:
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"""Use the tool."""
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values = query.split(",")
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person = values[0]
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if len(values) > 1:
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num_results = int(values[1])
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else:
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num_results = 2
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return self._search(person, num_results)
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