Create other_tools.py

other_tools.py

@@ -0,0 +1,92 @@
+from langchain.tools import tool
+#mathematical operations
+import cmath
+@tool(description="Multiplies two numbers.")
+def multiply(a: float, b: float) -> float:
+    return a * b
+
+@tool(description="Adds two numbers.")
+def add(a: float, b: float) -> float:
+    return a + b
+
+@tool(description="Subtracts two numbers.")
+def subtract(a: float, b: float) -> int:
+    return a - b
+
+@tool(description="Divides two numbers.")
+def divide(a: float, b: float) -> float:
+    if b == 0:
+        raise ValueError("Cannot divided by zero.")
+    return a / b
+
+@tool(description="Get the modulus of two numbers.")
+def modulus(a: int, b: int) -> int:
+    return a % b
+
+@tool(description="Get the power of two numbers.")
+def power(a: float, b: float) -> float:
+    return a**b
+
+@tool(description="Get the square root of a number.")
+def square_root(a: float) -> float | complex:
+    if a >= 0:
+        return a**0.5
+    return cmath.sqrt(a)
+
+from langchain_community.document_loaders import WikipediaLoader
+
+@tool(description="Search Wikipedia for a query and return up to 2 results.")
+def wiki_search(query: str) -> dict:
+    print(f"Searching Wikipedia for: {query}")
+    
+    try:
+        # Load up to 2 documents from Wikipedia
+        search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
+
+        # Format the output for each document
+        formatted_search_docs = "\n\n---\n\n".join(
+            [
+                f'<Document source="{doc.metadata.get("source", "")}" page="{doc.metadata.get("page", "")}">\n{doc.page_content}\n</Document>'
+                for doc in search_docs
+            ]
+        )
+
+        print(f"Here are the outputs:\n{formatted_search_docs}")
+        return {"wiki_results": formatted_search_docs}
+
+    except Exception as e:
+        print(f"Error occurred: {e}")
+        return {"wiki_results": f"None found for {query}, use the results from the other tools or similar questions obtained earlier."}
+
+
+from langchain_community.document_loaders import ArxivLoader
+@tool(description="Search Arxiv for a query and return maximum 3 result.")
+def arvix_search(query: str) -> str:
+    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", "")}"/>{doc.page_content[:1000]}</Document>'
+            for doc in search_docs
+        ])
+    return {"arvix_results": formatted_search_docs}
+
+from langchain_community.tools.tavily_search import TavilySearchResults
+@tool(description="Search Tavily for a query and return maximum 3 results.")
+def web_search(query: str) -> str:
+    search_docs = TavilySearchResults(max_results=3).invoke(query=query)
+    formatted_search_docs = "\n\n---\n\n".join(
+        [
+            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>{doc.page_content}</Document>'
+            for doc in search_docs
+        ])
+    return {"web_results": formatted_search_docs}
+
+from upload_metadata_n_setup_retrivers import retriever
+def vector_search(query:str) -> str:
+    search_docs = retriever.invoke(input=query)
+    formatted_search_docs = "\n\n---\n\n".join(
+    [
+            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>{doc.page_content}</Document>'
+            for doc in search_docs
+        ])
+    return formatted_search_docs