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"""LangGraph Agent"""
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
from langchain_groq import ChatGroq
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader
from langchain_community.document_loaders import ArxivLoader
from langchain_community.vectorstores import SupabaseVectorStore
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_core.tools import tool
from langchain.tools.retriever import create_retriever_tool
from supabase.client import Client, create_client
from langchain.tools import Tool
load_dotenv()
def multiply(a: int, b: int) -> int:
return a * b
multiply_tool = Tool(
name="multiply",
func=multiply,
description="Multiply two numbers. Args (a: first int, b: second int)"
)
def add(a: int, b: int) -> int:
return a + b
add_tool = Tool(
name="add",
func=add,
description="Add two numbers. Args (a: first int, b: second int)"
)
def substract(a: int, b: int) -> int:
return a - b
substract_tool = Tool(
name="substract",
func=substract,
description="Substract two numbers. Args (a: first int, b: second int)"
)
def divide(a: int, b: int) -> int:
if b == 0:
raise ValueError("Cannot divide by zero.")
return a / b
divide_tool = Tool(
name="divide",
func=divide,
description="Divide two numbers. Args (a: first int, b: second int)"
)
def modulus(a: int, b: int) -> int:
return a % b
modulus_tool = Tool(
name="modulus",
func=modulus,
description="Modulus two numbers. Args (a: first int, b: second int)"
)
def wiki_search(query: str) -> str:
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}
wiki_search_tool = Tool(
name="wiki_search",
func=wiki_search,
description="Search Wikipedia for a query and return maximum 2 results. Args (query: the search query)"
)
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", "")}"/>\n{doc.page_content}\n</Document>'
for doc in search_docs
])
return {"web_results": formatted_search_docs}
web_search_tool = Tool(
name="web_search",
func=web_search,
description="Search Tavily for a query and return maximum 3 results. Args (query: the search query)"
)
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}
arvix_search_tool = Tool(
name="arvix_search",
func=arvix_search,
description="Search Arxiv for a query and return maximum 3 result. Args (query: the search query)"
)
# 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)
tools = [
multiply_tool,
add_tool,
substract_tool,
divide_tool,
modulus_tool,
wiki_search_tool,
web_search_tool,
arvix_search_tool,
]
# Build graph function
def build_graph(provider: str = "google"):
"""Build the graph"""
# Load environment variables from .env file
if provider == "google":
# Google Gemini
chat = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
elif provider == "groq":
# Groq https://console.groq.com/docs/models
chat = ChatGroq(model="qwen-qwq-32b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
elif provider == "huggingface":
# TODO: Add huggingface endpoint
chat = ChatHuggingFace(
llm=HuggingFaceEndpoint(
url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
temperature=0,
),
)
else:
raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
# Bind tools to LLM
chat_with_tools = chat.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[0].page_content}",
# )
# return {"messages": [sys_msg] + state["messages"] + [example_msg]}
from langchain_core.messages import AIMessage
def retriever(state: MessagesState):
query = state["messages"][-1].content
similar_doc = vector_store.similarity_search(query, k=1)[0]
content = similar_doc.page_content
if "Final answer :" in content:
answer = content.split("Final answer :")[-1].strip()
else:
answer = content.strip()
return {"messages": [AIMessage(content=answer)]}
# 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 = StateGraph(MessagesState)
#builder.add_node("retriever", retriever)
# Retriever ist Start und Endpunkt
#builder.set_entry_point("retriever")
#builder.set_finish_point("retriever")
# Compile graph
#return builder.compile()
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")
# Compile graph
return builder.compile() |