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.env

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+GOOGLE_API_KEY = "AIzaSyDB2kx888D7HUuya3vvb0wvHzDUP2BeC7M"
+TAVILY_API_KEY = "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"

.gitignore

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+.env

llm_agent.py

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+import os
+from dotenv import load_dotenv
+from langgraph.graph import START, StateGraph, MessagesState
+from langgraph.prebuilt import tools_condition
+from langgraph.prebuilt import ToolNode
+from langchain_google_genai import ChatGoogleGenerativeAI,GoogleGenerativeAIEmbeddings
+from langchain_community.document_loaders import WikipediaLoader
+from langchain_community.document_loaders import ArxivLoader
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain_community.vectorstores import FAISS
+from langchain_core.messages import SystemMessage,HumanMessage
+from langchain_core.tools import tool
+
+
+load_dotenv()
+
+os.environ["GOOGLE_API_KEY"] = os.getenv("GOOGLE_API_KEY")
+
+@tool
+def add(a:int,b:int)->int:
+    """Add two Numbers
+     Args:
+        a:int
+        b:int
+
+    """
+    return a+b
+
+@tool
+def subtract(a:int,b:int)->int:
+    """Subtract two numbers
+    Args:
+        a:int
+        b:int 
+    """
+    return a-b
+
+@tool
+def multiply(a:int,b:int)->int:
+    """Multiply Two Numbers
+     Args:
+        a:int
+        b:int
+    """
+    return a*b
+
+@tool 
+def divide(a:int,b:int)->int:
+    """Divide two numbers
+     Args:
+        a:int
+        b:int
+    """
+    if b==0:
+        raise ValueError("Cannot Divide by Zero")
+    return a//b
+
+@tool 
+def modulus(a:int,b:int)->int:
+    """Modulus of the two numbers
+    Args:
+        a:int
+        b:int
+    """
+    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 : str
+    """
+    print(query)
+
+    search_docs = WikipediaLoader(query=query,load_max_docs=2).load()
+
+    return {"wiki_results": 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(input=query)
+  
+    return {"web_results": search_docs}
+
+@tool
+def arxiv_search(query:str)->str:
+    """"search Arxiv for a query and return maximum 3 results
+    Args:
+        query: search query
+    """
+
+    search_docs = ArxivLoader(query=query,load_max_docs = 3).load()
+   
+    return {"arxiv_resutls": search_docs}
+
+
+system_prompt = """
+You are a helpful assistant tasked with answering questions using a set of tools. 
+Now, I will ask you a question. Report your thoughts, and finish your answer with the following template: 
+FINAL ANSWER: [YOUR FINAL ANSWER]. 
+YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
+Your answer should only start with "FINAL ANSWER: ", then follows with the answer. 
+"""
+
+sys_msg = SystemMessage(content=system_prompt)
+
+
+embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+
+
+
+tools = [
+    add,
+    subtract,
+    multiply,
+    divide,
+    modulus,
+    wiki_search,
+    web_search,
+    arxiv_search,
+]
+
+
+
+def build_graph():
+    llm = ChatGoogleGenerativeAI(model = "gemini-2.0-flash")
+    print(tools)
+    # Bind tools to LLM
+    llm_with_tools = llm.bind_tools(tools)
+
+    # Node
+    def assistant(state: MessagesState):
+        """Assistant node"""
+        return {"messages": [llm_with_tools.invoke(state["messages"])]}
+    
+    # def retriever(state: MessagesState):
+    #     """Retriever node"""
+    #     similar_question = vector_store.similarity_search(state["messages"][0].content)
+    #     example_msg = HumanMessage(
+    #         content=f"Here I provide a similar question and answer for reference: \n\n{similar_question}",
+    #     )
+    #     return {"messages": [sys_msg] + state["messages"] + [example_msg]}
+
+    builder = StateGraph(MessagesState)
+    # builder.add_node("retriever", retriever)
+    builder.add_node("assistant", assistant)
+    builder.add_node("tools", ToolNode(tools))
+    builder.add_edge(START, "assistant")
+    # builder.add_edge("retriever", "assistant")
+    builder.add_conditional_edges(
+        "assistant",
+        tools_condition,
+    )
+    builder.add_edge("tools", "assistant")
+
+    # Compile graph
+    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 = build_graph()
+    # Run the graph
+    messages = [HumanMessage(content=question)]
+    messages = graph.invoke({"messages": messages})
+    for m in messages["messages"]:
+        m.pretty_print()