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from langchain_community.retrievers import BM25Retriever
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from langchain.tools import Tool
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from langchain_community.tools import DuckDuckGoSearchRun
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from huggingface_hub import list_models
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import random
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from retriever import docs
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import requests
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bm25_retriever = BM25Retriever.from_documents(docs)
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def extract_text(query: str) -> str:
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"""Retrieves detailed information about gala guests based on their name or relation."""
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results = bm25_retriever.invoke(query)
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if results:
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return "\n\n".join([doc.page_content for doc in results[:3]])
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else:
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return "No matching guest information found."
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guest_info_tool = Tool(
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name="guest_info_retriever",
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func=extract_text,
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description="Retrieves detailed information about gala guests based on their name or relation."
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)
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search_tool = DuckDuckGoSearchRun()
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def get_weather_info(location: str) -> str:
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"""Fetches weather information from wttr.in for a given location."""
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url = f"https://wttr.in/{location}?format=3"
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try:
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response = requests.get(url, timeout=10)
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return response.text
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except Exception as e:
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return f"Error fetching weather: {str(e)}"
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weather_info_tool = Tool(
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name="get_weather_info",
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func=get_weather_info,
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description="Fetches dummy weather information for a given location."
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)
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def get_hub_stats(author: str) -> str:
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"""Fetches the most downloaded model from a specific author on the Hugging Face Hub."""
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try:
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models = list(list_models(author=author, sort="downloads", direction=-1, limit=1))
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if models:
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model = models[0]
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return f"The most downloaded model by {author} is {model.id} with {model.downloads:,} downloads."
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else:
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return f"No models found for author {author}."
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except Exception as e:
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return f"Error fetching models for {author}: {str(e)}"
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hub_stats_tool = Tool(
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name="get_hub_stats",
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func=get_hub_stats,
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description="Fetches the most downloaded model from a specific author on the Hugging Face Hub."
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) |