tool & agent

agent.py

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+import os
+from typing import TypedDict, Annotated
+from dotenv import load_dotenv
+from langgraph.graph.message import add_messages
+from langchain_core.messages import AnyMessage, HumanMessage, AIMessage
+from langgraph.prebuilt import ToolNode
+from langgraph.graph import START, StateGraph, MessagesState
+from langgraph.prebuilt import tools_condition
+from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_groq import ChatGroq
+from langchain_openai import ChatOpenAI
+
+from tools import (
+    divide,
+    multiply,
+    modulus,
+    add,
+    subtract,
+    power,
+    square_root,
+    web_search,
+    wiki_search,
+    arxiv_search,
+)
+
+# load api key
+load_dotenv()
+
+
+def buildAgent(provider="huggingface"):
+    # Generate the chat interface, including the tools
+    if provider == "huggingface":
+        llm = ChatHuggingFace(
+            llm=HuggingFaceEndpoint(repo_id="Qwen/Qwen2.5-Coder-32B-Instruct"),
+        )
+    elif provider == "groq":
+        llm = ChatGroq(model="qwen-qwq-32b")
+    elif provider == "openrouter":
+        llm = ChatOpenAI(
+            base_url="https://openrouter.ai/api/v1",
+            api_key=os.environ.get("OPENROUTER_API_KEY"),
+            model="google/gemini-2.0-flash-exp",
+        )
+
+    tools = [
+        multiply,
+        add,
+        subtract,
+        divide,
+        modulus,
+        power,
+        square_root,
+        web_search,
+        wiki_search,
+        arxiv_search,
+    ]
+
+    chat_with_tools = llm.bind_tools(tools)
+
+    # nodes
+    def assistant(state: MessagesState):
+        return {
+            "messages": [chat_with_tools.invoke(state["messages"])],
+        }
+
+    ## The graph
+    builder = StateGraph(MessagesState)
+    # Define nodes: these do the work
+    builder.add_node("assistant", assistant)
+    builder.add_node("tools", ToolNode(tools))
+    # Define edges: these determine how the control flow moves
+    builder.add_edge(START, "assistant")
+    builder.add_conditional_edges(
+        "assistant",
+        # If the latest message requires a tool, route to tools
+        # Otherwise, provide a direct response
+        tools_condition,
+    )
+    builder.add_edge("tools", "assistant")
+    return builder.compile()
+
+
+if __name__ == "__main__":
+    question = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
+    graph = buildAgent(provider="groq")
+    messages = [HumanMessage(content=question)]
+    print(messages)
+    messages = graph.invoke({"messages": messages})
+    for m in messages["messages"]:
+        m.pretty_print()

tools.py

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+import cmath
+from langchain_core.tools import tool
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain_community.document_loaders import WikipediaLoader
+from langchain_community.document_loaders import ArxivLoader
+
+@tool
+def multiply(a: int, b: int) -> int:
+    """Multiply two numbers.
+    Args:
+        a: first int
+        b: second int
+    """
+    return a * b
+
+@tool
+def add(a: int, b: int) -> int:
+    """Add two numbers.
+        Args:
+        a: first int
+        b: second int
+    """
+    return a - b
+
+@tool
+def subtract(a: int, b: int) -> int:
+    """Subtract two numbers.
+    
+    Args:
+        a: first int
+        b: second int
+    """
+    return a - b
+
+@tool
+def divide(a: int, b: int) -> int:
+    """Divide two numbers.
+    
+    Args:
+        a: first int
+        b: second int
+    """
+    if b == 0:
+        raise ValueError("Cannot divide by zero.")
+    return a / b
+
+@tool
+def modulus(a: int, b: int) -> int:
+    """Get the modulus of two numbers.
+    
+    Args:
+        a: first int
+        b: second int
+    """
+    return a % b
+
+@tool
+def power(a: float, b: float) -> float:
+    """
+    Get the power of two numbers.
+    Args:
+        a (float): the first number
+        b (float): the second number
+    """
+    return a**b
+
+
+@tool
+def square_root(a: float) -> float | complex:
+    """
+    Get the square root of a number.
+    Args:
+        a (float): the number to get the square root of
+    """
+    if a >= 0:
+        return a**0.5
+    return cmath.sqrt(a)
+
+@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 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 arxiv_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 {"arxiv_results": formatted_search_docs}