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from langchain_core.tools import tool
from langchain_community.document_loaders import WikipediaLoader
from langchain_community.document_loaders import ArxivLoader
# Search engine specifically for LLMs
# from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_tavily import TavilySearch
@tool
def web_search(query: str) -> str:
"""Search Tavily for a query and return maximum 3 results.
Args:
query: The search query."""
# print(f"Web search query:::::::::::: {query}")
search_docs = TavilySearch(max_results=3).invoke({"query":query})
formatted_search_docs = "\n\n---\n\n".join(
[
f'<Document source="{doc["url"]}" page="{doc["title"]}"/>\n{doc["content"]}\n</Document>'
for doc in search_docs['results']
])
# print(f"Web search result:::::::::::: {formatted_search_docs}")
return {"web_results": formatted_search_docs}
@tool
def wiki_search(query: str) -> str:
"""Search Wikipedia for a query and return maximum 2 results.
Args:
query: The search query."""
search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
formatted_search_docs = "\n\n---\n\n".join(
[
f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
for doc in search_docs
])
return {"wiki_results": formatted_search_docs}
@tool
def arvix_search(query: str) -> str:
"""Search Arxiv for a query and return maximum 3 result.
Args:
query: The search query."""
search_docs = ArxivLoader(query=query, load_max_docs=3).load()
formatted_search_docs = "\n\n---\n\n".join(
[
f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>'
for doc in search_docs
])
return {"arvix_results": formatted_search_docs} |