agent.py

from langchain_core.tools import tool
from langchain.tools.retriever import create_retriever_tool
from langchain_community.document_loaders import WikipediaLoader
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import ArxivLoader

from langchain_core.messages import HumanMessage, SystemMessage

from langchain_ollama import ChatOllama
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_huggingface import HuggingFaceEmbeddings, ChatHuggingFace, HuggingFaceEndpoint
from langgraph.graph import START, StateGraph, MessagesState

# from langchain_chroma import Chroma
import faiss
from langchain_community.docstore.in_memory import InMemoryDocstore
from langchain_community.vectorstores import FAISS

from langgraph.prebuilt import ToolNode
from langgraph.prebuilt import tools_condition

import os
from dotenv import load_dotenv


load_dotenv()


@tool
def multiply(a: int, b: int) -> int:
    """Multiply two numbers and return the result.

    Args:
        a (int): The first number.
        b (int): The second number.
    Returns:
        int: The product of the two numbers.
    """
    return a * b

@tool
def add(a: int, b: int) -> int:
    """Add two numbers and return the result.
    Args:
        a (int): The first number.
        b (int): The second number.
    Returns:
        int: The sum of the two numbers.
    """
    return a + b

@tool
def subtract(a: int, b: int) -> int:
    """Subtract two numbers and return the result.
    Args:
        a (int): The first number.
        b (int): The second number.
    Returns:
        int: The difference between the two numbers.
    """
    return a - b

@tool
def divide(a: int, b: int) -> int:
    """Divide two numbers and return the result.
    Args:
        a (int): The first number.
        b (int): The second number.
    Returns:
        int: The quotient of the two numbers.
    """
    return a / b

@tool
def modulus(a: int, b: int) -> int:
    """Calculate the modulus of two numbers and return the result.
    Args:
        a (int): The first number.
        b (int): The second number.
    Returns:
        int: The modulus of the two numbers.
    """
    return a % b

@tool
def wiki_search(query: str) -> str:
    """Search Wikipedia for a given query and return the top result.
    Args:
        query (str): The search query.
    """
    search_docs = WikipediaLoader(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 (str): The search query.
    """
    search_docs = TavilySearchResults(max_results=3).invoke(query)
    formatted_search_docs = '\n\n---\n\n'.join(
        [
            f'<Document source="{doc["url"]}" page="{doc.get("title", "")}">\n{doc.get("content", "")}\n</Document>' for doc in search_docs
        ]
    )
    return {'web_results': formatted_search_docs}

@tool
def arvix_search(query: str) -> str:
    """Search Arvix for a query and return maximum 3 results

    Args:
        query (str): The search query.
    """
    search_docs = ArxivLoader(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}\n</Document>' for doc in search_docs
        ]
    )
    return {'arvix_results': formatted_search_docs}


# load the system prompt from the file
with open("system_prompt.txt", "r", encoding="utf-8") as f:
    system_prompt = f.read()


# System message
sys_msg = SystemMessage(content=system_prompt)

# Retriever
embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-small-en-v1.5")

# vector_store = Chroma(
#     collection_name="demo_collection",
#     embedding_function=embeddings,
#     persist_directory="./chroma_langchain_db",
# )

embedding_dim = len(embeddings.embed_query("hello world"))
index = faiss.IndexFlatL2(embedding_dim)

vector_store = FAISS(
    embedding_function=embeddings,
    index=index,
    docstore=InMemoryDocstore(),
    index_to_docstore_id={},
)
create_retriever_tool = create_retriever_tool(
    retriever= vector_store.as_retriever(),
    name='Question Search',
    description='A tool to retrieve similar question from vector store.'
)


tools = [
    multiply,
    add,
    subtract,
    modulus,
    wiki_search,
    web_search,
    arvix_search
]

# build graph function
def build_graph(tag: str='huggingface'):
    """Build the graph"""

    if tag == 'local':
        llm = ChatOllama(model="qwen3")
    elif tag == 'google':
        # Google Gemini
        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
    elif tag == "huggingface":
        llm = ChatHuggingFace(
            llm=HuggingFaceEndpoint(
                endpoint_url="https://api-inference.huggingface.co/models/Qwen/Qwen3-14B"),
            temperature=0,
        )
    else:
        raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
    # bind tools to llm
    llm_with_tools = llm.bind_tools(tools)

    def assistant(state: MessagesState):
        return {'messages': [llm_with_tools.invoke(state['messages'])]}

    def retriever(state: MessagesState):
        similar_question = vector_store.similarity_search(state['messages'][0].content)
        example_msg = HumanMessage(
            content=f''
        )
        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, 'retriever')
    builder.add_edge('retriever', 'assistant')
    builder.add_conditional_edges(
        'assistant',
        tools_condition
    )
    builder.add_edge('tools', 'assistant')

    # builder.set_entry_point("retriever")
    # builder.set_finish_point("retriever")

    return builder.compile()


# test
if __name__ == "__main__":
    question = 'When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?'
    # build the graph
    graph = build_graph('local')
    # run the graph
    messages = [HumanMessage(content=question)]
    messages = graph.invoke({'messages': messages})
    for m in messages['messages']:
        m.pretty_print()