Create tools.py

tools.py

@@ -0,0 +1,137 @@
+from langchain_core.tools import tool
+from langchain_community.document_loaders import WikipediaLoader
+from langchain_community.document_loaders import ArxivLoader
+from langchain_community.tools.tavily_search import TavilySearchResults
+from difflib import SequenceMatcher
+
+@tool
+def add(a: float, b: float) -> float:
+    """
+    Adds two numbers.
+    Args:
+        a (float): the first number
+        b (float): the second number
+    """
+    return a + b
+
+@tool
+def subtract(a: float, b: float) -> float:
+    """
+    Subtracts two numbers.
+    Args:
+        a (float): the first number
+        b (float): the second number
+    """
+    return a - b
+    
+@tool
+def multiply(a: float, b: float) -> float:
+    """
+    Multiplies two numbers.
+    Args:
+        a (float): the first number
+        b (float): the second number
+    """
+    return a * b
+
+@tool
+def divide(a: float, b: float) -> float:
+    """
+    Divides two numbers.
+    Args:
+        a (float): the first float number
+        b (float): the second float number
+    """
+    if b == 0:
+        raise ValueError("Cannot divided by zero.")
+    return a / b
+
+@tool
+def modulus(a: int, b: int) -> int:
+    """
+    Get the modulus of two numbers.
+    Args:
+        a (int): the first number
+        b (int): the second number
+    """
+    return a % b
+
+@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 web_search(query: str) -> str:
+    """Search Tavily for a query and return maximum 3 results.
+    
+    Args:
+        query: The search query."""
+    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", "")}"/>\n{doc.page_content}\n</Document>'
+            for doc in search_docs
+        ])
+    return {"web_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}
+
+@tool
+def search_metadata(query: str) -> str:
+    """Search through metadata.jsonl file for matching questions and answers.
+    
+    Args:
+        query: The search query to match against questions in the metadata file.
+    """
+    import json
+    
+    results = []
+    with open("metadata.jsonl", "r", encoding="utf-8") as f:
+        for line in f:
+            try:
+                data = json.loads(line)
+                # Calculate similarity ratio
+                similarity = SequenceMatcher(None, query.lower(), data["Question"].lower()).ratio()
+                if similarity > 0.6:  # Threshold for similarity
+                    results.append({
+                        "question": data["Question"],
+                        "answer": data["Final answer"],
+                        "steps": data["Annotator Metadata"]["Steps"],
+                        "similarity": similarity
+                    })
+            except json.JSONDecodeError:
+                continue
+    
+    if not results:
+        return "No matching results found in metadata."
+    
+    # Sort by similarity
+    results.sort(key=lambda x: x["similarity"], reverse=True)
+    
+    formatted_results = "\n\n---\n\n".join(
+        [f"Question: {r['question']}\nAnswer: {r['answer']}\nSteps: {r['steps']}" 
+         for r in results[:3]]  # Return top 3 most similar results
+    )
+    return formatted_results